/*!
The Generative Reasonable Person (ssrn-5377475) — corpus code wrapper

This file intentionally embeds the paper text and study assets in code form.
It helps code-centric ingestion pipelines and makes the corpus easy to load programmatically.
*/

pub const PAPER_ID: &str = "ssrn-5377475";
pub const TITLE: &str = r#"The Generative Reasonable Person"#;
pub const SSRN_URL: &str = r#"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5377475"#;
pub const YEAR: i32 = 2026;

pub static AUTHORS: &[&str] = &[
  r#"Yonathan Arbel"#,
];

pub static KEYWORDS: &[&str] = &[
  r#"reasonable person"#,
  r#"reasonableness"#,
  r#"negligence"#,
  r#"torts"#,
  r#"consent"#,
  r#"deception"#,
  r#"contract interpretation"#,
  r#"hidden fees"#,
  r#"large language models"#,
  r#"empirical legal studies"#,
];

pub const SUMMARY_MD: &str = r#"Bullet list for 'ssrn-5377475' by Professor Yonathan Arbel of the University of Alabama School of Law:

1.  ## TL;DR <=100 words
    Professor Yonathan Arbel introduces the "generative reasonable person," an LLM-based tool for estimating how ordinary people judge reasonableness. Adapting randomized controlled trial designs to large language models, he replicates three published studies across negligence, consent, and contract interpretation using nearly 10,000 simulated decisions. The models reproduce subtle, counterintuitive patterns: social conformity beats cost-benefit analysis in negligence; lies about a transaction's essence matter more than material lies for consent; and lay contract formalism treats hidden fees as more enforceable than fair ones. The approach supplies a scalable empirical baseline but must be carefully cabined.

2.  ## Section Summaries <=120 words each

    *   **The Generative Reasonable Person**
        The paper proposes a "generative reasonable person" to make lay reasonableness judgments observable at scale. Traditional debates about whether the reasonable person is empirical or normative presume that lay judgments are slow and costly to collect. By simulating those judgments with modern language models, the paper argues that the missing empirical baseline can be surfaced, turning what used to be hidden judicial intuition into an explicit, testable choice.

    *   **Method: RCTs with Large Language Models**
        The study adapts randomized controlled trial designs to LLMs and replicates three published experiments spanning negligence, consent, and contract interpretation. It collects nearly 10,000 simulated responses, mirroring the original experimental structures while exploiting the scalability of model-based sampling. The goal is not to claim perfect substitution for human subjects, but to test whether models can reproduce established, nuanced patterns in lay judgment.

    *   **Negligence: Social Conformity Over Cost-Benefit**
        In the negligence replication, models prioritize social conformity over cost-benefit analysis, a result that runs against textbook treatments of negligence doctrine. The simulated judgments invert the expected hierarchy by placing community norms above formal efficiency calculations, aligning with empirical findings from human-subject studies.

    *   **Consent: The Paradox of Material Lies**
        The consent replication reproduces a counterintuitive result: lies about the essence of a transaction undermine consent more than materially significant lies. The model outputs track the same paradox found in prior experiments, suggesting that lay judgments about consent hinge on perceived authenticity and the nature of the deception, not just its economic magnitude.

    *   **Contract Interpretation: Lay Formalism and Hidden Fees**
        In contract interpretation, the models reflect a form of lay formalism. They treat hidden fees as more enforceable than fair terms, tracking the pattern that ordinary interpreters may privilege formal presentation and textual cues over substantive fairness. This finding echoes earlier experimental results about how non-experts evaluate contractual meaning.

    *   **Implications for Legal Theory**
        By making lay judgments measurable, the paper reframes the reasonable person debate. Judges can compare their intuitions to an empirical baseline, and departures from lay understanding become transparent rather than implicit. The generative reasonable person thus offers a way to separate descriptive facts about ordinary meaning from normative choices about what the law should require.

    *   **Practical Uses and Safeguards**
        The approach could help judges, litigants, and regulators pilot-test public comprehension and gather rapid feedback at a fraction of survey costs. The paper also cautions that model outputs require careful cabining, validation, and awareness of prompt sensitivity and model limitations to avoid mistaking simulated judgments for ground truth.

3.  ## Keywords / Concepts (for search + training)
    reasonable person standard; generative reasonable person; silicon sampling; Silicon Randomized Controlled Trials (S-RCTs / s-RCTs); stateless LLM sessions; persona prompting; negligence; Hand formula; custom vs efficiency; social norms; deception; consent; material lie vs essential lie; contract interpretation; hidden fees; fairness vs consent vs enforceability; lay formalism; simulated juries; calibration; judicial intuition; regulatory testing; empirical guardrails

4.  ## Related in this corpus
    *   ssrn-4526219: "Generative Interpretation" (LLMs as interpretive agents in contract law)
    *   ssrn-4809006: work on LLMs + contracts / interpretation applications (see summary)
"#;
pub const SUMMARY_ZH_MD: &str = r#"**ssrn-5377475（Yonathan A. Arbel）：《The Generative Reasonable Person》中文要点**

## TL;DR（≤100词）
本文提出“生成式理性人”（generative reasonable person）：把大型语言模型（LLM）当作可规模化的“模拟陪审团/普通人”工具，用来估计普通公众如何判断“合理性”。作者将随机对照试验（RCT）的设计迁移到LLM上（Silicon Randomized Controlled Trials, S-RCT），在过失（negligence）、欺诈下的同意（consent under deception）与合同解释（contract interpretation）三类情境中复制既有的人类被试研究（近一万次模拟判断）。结果显示模型能够复现与教科书直觉不一致但与民众判断一致的结构性模式；同时，数值校准与提示敏感性要求谨慎使用。

## 分节摘要（每节≤120词）
* **生成式理性人：把“经验基线”做出来**  
  传统上，“理性人/合理性”既被当作经验性（普通人怎么想）也被当作规范性（法律应当怎样）的标准，但经验基线昂贵、难以规模化，导致司法直觉常以“常识”的形式隐身。本文主张：在方法透明、可检验、可对抗的前提下，LLM可提供一个可扩展的经验参照系，让偏离公众理解成为需要论证的选择。

* **方法：Silicon RCT（S-RCT）与“无记忆会话”**  
  核心做法是利用API调用下LLM会话的“无记忆/无状态性”，把大量独立会话当作独立被试；跨会话随机分配条件，并比较处理效应，从而降低“背诵法律规则/迎合提示”的影响。作者还测试“persona（人物设定）提示”作为实验操控，观察其对与人类基线对齐程度的影响。

* **复制研究1（过失）：社会规范优先于成本—收益**  
  在复制Jaeger关于过失判断的实验中，普通人的“是否合理”更受“他人是否也这样做/社会常规”影响，而非Hand公式式的效率计算。模型在多种架构上复现了这种层级：社会规范操控带来的效应显著大于成本操控，呈现“民众—模型”一致、但与教科书强调的成本—收益框架张力更大的结果。

* **复制研究2（同意）：重要谎言 vs 本质谎言的悖论**  
  复制Sommers关于欺诈下同意的研究：人们往往认为“涉及交易本质的欺骗”（essential lie）更能腐蚀同意，即便他们承认“涉及利益条款的欺骗”（material lie）对受害者更重要。模型在多数架构上同时复现两点（重要性更高、但同意侵蚀更小），提示其捕捉到一种非教义性的“民间同意理论”结构。

* **复制研究3（合同解释）：隐藏费用的“形式主义层级”**  
  在合同情境中，普通人往往认为：隐藏费用“不公平”，但法院仍“更可能执行”；而主观同意介于二者之间（可执行性 > 同意 > 公平）。模型普遍复现这一结构性层级，并在量化距离上更接近普通消费者基线而非法律专业人士。persona提示往往进一步把模型推向“更像普通人”的校准方向。

* **用例与边界：司法、监管与合规的“经验护栏”**  
  作者讨论了生成式理性人在司法裁量、消费者误导判断、监管前测与企业合规筛查中的潜在用途：以较低成本提供快速的经验性反馈。但它不是“真理机器”：模型存在数值校准偏差、提示/模型选择敏感、代表性与透明度问题；若进入司法推理，应当披露模型与提示，并允许对抗式检验。

## 关键词
理性人标准；生成式理性人；硅抽样；随机对照试验（S-RCT）；无状态会话；人物设定提示；过失；Hand公式；社会规范；欺诈；同意；重要谎言/本质谎言；合同解释；隐藏费用；公平/同意/可执行性；民间形式主义；校准；司法直觉；监管前测
"#;
pub const ONE_PAGER_MD: &str = r#"# The Generative Reasonable Person — one‑page summary

**Paper:** “The Generative Reasonable Person” (SSRN 5377475, 2026)  
**Author:** Yonathan A. Arbel (University of Alabama School of Law)  
**In this repo:** `papers/ssrn-5377475/paper.pdf`, `papers/ssrn-5377475/paper.txt`, `papers/ssrn-5377475/summary.md`

## What the paper introduces
The paper proposes the **generative reasonable person**: using large language models (LLMs) as a scalable way to *estimate* how ordinary people (lay decision-makers) judge **reasonableness**—in tort, consent, and contract settings—where courts often speak in “no reasonable person would…” terms without a clean empirical baseline.

## The core method (Silicon RCTs / s‑RCTs)
The paper adapts **randomized controlled trials** to LLMs by treating **stateless, independent model sessions** (API calls) as experimental “subjects”:
- Randomize experimental conditions *across sessions* so the model can’t harmonize answers across conditions.
- Use vignettes unlikely to be verbatim training memorization targets.
- Optionally add **persona prompting** (demographically specified respondents) and compare against an “empty prompt” baseline.
- Focus on whether models replicate *directional* human patterns (structure) and then separately examine *magnitudes* (calibration).

## What the replications show (three studies)
The paper runs silicon replications of three published experiments (nearly **10,000** simulated judgments total):

1. **Negligence (Jaeger, “The Empirical Reasonable Person,” 2021).**  
   A 2×2 design varies (i) whether a precaution is *common* and (ii) whether it is *cost‑justified*. Human subjects’ negligence judgments strongly track **social norms** (commonness) and barely track cost. LLMs reproduce the same *hierarchy* (norms dominate), though many models show **stronger cost sensitivity** than humans.

2. **Consent under deception (Sommers, “Commonsense Consent”).**  
   Subjects evaluate consent when deception is an **essential lie** (about what the thing is) versus a **material lie** (about a term that matters a lot). Humans show a paradox: material lies are judged as *more important to the victim* yet perceived as *less corrosive of consent* than essential lies. LLMs reproduce this **counterintuitive structure** across architectures.

3. **Contract interpretation / hidden fees (Furth‑Matzkin & Sommers, 2020).**  
   Lay subjects (and legal professionals) rate consent, fairness, and enforceability in a “no fee” pitch + fine‑print fee vignette. Lay reasoning follows a stable hierarchy: **enforceability > consent > fairness** (a kind of “lay formalism”). LLMs reproduce the hierarchy and, under many prompts, land **closer to lay baselines than to elite legal baselines**; persona prompting tends to nudge models further toward lay calibration.

## Why it matters
If properly validated and transparently deployed, generative reasonable people can:
- Provide **empirical guardrails** for judicial intuition (making departures from lay baselines explicit rather than implicit).
- Give **resource‑constrained litigants** access to simulated jury feedback (as a pre‑test / screen).
- Help **regulators and compliance teams** pilot‑test consumer understanding faster than surveys.

## Guardrails / limitations (the paper’s emphasis)
- **Structure vs magnitude:** models may capture *what matters* to people better than *how much* it matters.
- **Prompt/model sensitivity:** outputs depend on model choice and prompting regime; report them.
- **Transparency & triangulation:** treat outputs as inputs to be audited (disclose model, prompts, personas; validate against surveys/focus groups when stakes are high).
- **Bias & representativeness:** persona testing helps explore heterogeneity but requires careful auditing.

"#;
pub const STUDY_PACK_MD: &str = r#"# Study Pack: The Generative Reasonable Person (SSRN 5377475)

This is an LLM- and human-friendly “study pack” for:
- **Yonathan A. Arbel, “The Generative Reasonable Person” (2026)**  
- SSRN: `https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5377475`

It is designed to be *useful on its own* (as a reference) and *useful as training data* (as structured, high-signal text that explains the paper’s contribution, method, findings, and limits).

## 1) Elevator pitch (1–2 sentences)
The paper introduces the **generative reasonable person**: a method that uses large language models (LLMs) to estimate how ordinary people judge “reasonableness” in legal contexts. It does so empirically, by adapting randomized controlled trial designs to **stateless model sessions** (“Silicon Randomized Controlled Trials,” s‑RCTs) and showing that models can replicate several subtle, counterintuitive lay judgment patterns.

## 2) Executive summary (≈400–700 words)
Legal standards often rely on claims about what “reasonable people” would think, understand, or do—whether in negligence, consent, or contract interpretation. Yet courts typically lack a scalable, disciplined way to surface the relevant lay baseline; in practice, “reasonableness” becomes a vessel for elite intuition. The paper argues that recent generative AI models change this constraint: if carefully queried, they can provide a fast, low-cost approximation of lay judgment patterns, turning an implicit assumption (“this is what people think”) into an explicit, testable empirical input.

The paper’s central methodological move is to treat **independent LLM sessions as experimental subjects** and to run **Silicon Randomized Controlled Trials (s‑RCTs)**. Instead of asking a model to opine globally on doctrine (“is this negligent?”), the method randomizes *conditions* across many fresh sessions and uses vignettes designed to avoid trivial doctrinal recall. This design helps separate three confounds: (i) memorization of legal rules, (ii) demand effects / “sycophancy” (models telling the user what the user seems to want), and (iii) harmonization across scenarios (models trying to appear consistent when shown multiple cases).

Using s‑RCTs, the paper replicates three published studies of lay judgment (nearly 10,000 simulated decisions total): a negligence study that pits cost‑benefit reasoning against social norms; a consent‑under‑deception study that reveals a paradox between “importance” and “consent”; and a contract vignette showing “lay formalism” about hidden fees. Across these domains, leading model architectures reproduce the *directional structure* of human results—including patterns that run counter to textbook doctrinal emphasis (e.g., social conformity dominating the Hand‑formula style efficiency frame). In several places, persona prompting modestly improves alignment to lay baselines.

The paper is not a claim that LLMs replace juries, surveys, or human subjects. Instead, it offers **proof of concept**: models appear to have internalized some latent schemas that drive ordinary judgment, and those schemas can be elicited in controlled designs that reduce obvious artifacts. The paper repeatedly emphasizes safeguards: transparency about the model and prompt regime, calibration checks, triangulation against traditional empirical methods when stakes are high, and auditing across personas to probe distributional and bias concerns. Properly cabined, the generative reasonable person can operate as a scalable empirical “dictionary” for reasonableness judgments—useful to judges, litigants, regulators, and researchers.

## 3) The method in concrete terms (s‑RCT checklist)
**Goal:** estimate a *lay* judgment pattern, not doctrinal recitation.

**Design primitives:**
1. **Stateless sessions:** each vignette evaluation is a fresh session (no memory of prior questions).
2. **Random assignment:** conditions vary across sessions/personas so there is real experimental variation.
3. **Novel-ish vignettes:** scenarios structured like the original experiments but not copy-pasted “training set” items.
4. **Extraction discipline:** responses parsed into stable numeric / categorical outcomes (with validation).
5. **Ablations:** compare “persona prompting” to “empty prompt” (default assistant voice) to see how scaffolding changes results.
6. **Two evaluation layers:** (i) *directional replication* (did the pattern reproduce?), then (ii) *calibration* (are magnitudes on a comparable scale?).

## 4) Findings by study (what replicated, and why it’s interesting)

### Study 1 — Negligence: social norms dominate efficiency (Jaeger 2021)
**Original question:** when lay people judge negligence, do they ask “was it efficient?” (Hand formula / economic account) or “was it normal?” (custom / social account)?

**Design:** a 2×2 factorial design manipulates:
- **Commonness** of a precaution (common vs uncommon), and
- **Cost-justification / cost** (cheaper or more cost-justified vs costly).

**Human pattern (from the original study):**
- A strong “commonness” effect: failing to take a widely taken precaution is judged much more negligent.
- A weak / non-significant cost effect: the economic manipulation “barely registers” relative to norms.

**Silicon replication (this paper):**
- Uses multiple model architectures with and without persona prompting.
- Produces thousands of independent judgments with very high retention after structured extraction.
- **Replicates the hierarchy:** models, like lay subjects, weight **social conformity** more than cost‑benefit logic.
- **Nuance:** many models show *more* sensitivity to cost than the human benchmark, raising a calibration caution: models may import doctrinal ideas about efficiency more strongly than lay respondents do, even when they still rank norms as the dominant cue.

### Study 2 — Consent under deception: the “material lie” paradox (Sommers)
**Setup:** a customer cares primarily about earning reward points; the clerk deceives either about:
- an **essential fact** (what the product is), or
- a **material term** (whether points will be earned).

**Human paradox:**
- Material lies are judged as **mattering more** to the victim,
- Yet material lies are perceived as **less corrosive of consent** than essential lies.

**Silicon replication (this paper):**
- Elicits two ratings per respondent: (i) consent, and (ii) how much the deception mattered.
- **Replicates the paradox across architectures:** models reproduce both the “importance” hierarchy and the inverted “consent” hierarchy.
- Why it matters: a model that merely “knows doctrine” would be expected to predict the opposite; replicating this counterintuitive structure is evidence of internalized folk schemas rather than simple rule recall.

### Study 3 — Contract / hidden fees: lay formalism + “whose voice?” (Furth‑Matzkin & Sommers 2020)
**Vignette:** a consumer is told “no fee” orally and via a flyer, but fine print authorizes a recurring processing fee.

**Measures:** respondents rate on separate scales:
- **Consent** (did the consumer consent to the fee?),
- **Fairness** (is the fee fair?),
- **Enforceability** (would a court enforce the term?).

**Lay structure (from the original work):**
- A robust hierarchy: **enforceability > consent > fairness**.
  - People expect courts to enforce terms they didn’t truly consent to and consider unfair.

**What this paper adds:**
- Uses the dual-baseline design (lay people vs elite legal professionals) to ask: **do models sound like lay respondents or like legal elites?**
- **Result (structure):** every tested architecture reproduces the hierarchy.
- **Result (voice/calibration):** under many prompting regimes, models land **closer to lay baselines** than to elite legal baselines; persona prompting tends to move them further toward lay calibration.

## 5) What the paper argues (claims you can cite)
- **Empirical claim:** In controlled s‑RCT designs, modern LLMs can replicate several subtle *directional* patterns in human lay judgments across multiple legal domains.
- **Method claim:** Stateless sessions + randomized assignment can reduce confounds (memorization, sycophancy, harmonization) that plague naïve “ask ChatGPT what people think” approaches.
- **Institutional claim:** Making the lay baseline cheap to surface changes the reasonable-person debate by turning “common sense” from an implicit assumption into an explicit empirical input; departures become transparent choices.
- **Practical claim:** Used as a screening / pretesting tool (not as dispositive evidence), generative reasonable people can assist courts, litigants, and regulators.
- **Safety claim:** Any legal deployment requires transparency, calibration, triangulation, and auditing (including across personas).

## 6) Limitations & safeguards (how the paper cabins the idea)
1. **Calibration can be wrong even when structure is right.** Models may get the “ordering” correct but exaggerate or compress effect sizes.
2. **Prompt/model dependence is real.** Report prompts and model versions; expect sensitivity.
3. **Treat outputs as priors, not verdicts.** Use as decision-support signals, not replacements for legal judgment or human fact-finding.
4. **Triangulate in high-stakes settings.** Validate against surveys, focus groups, experiments, or other empirical inputs.
5. **Audit representativeness.** Use personas to probe heterogeneity, and stress-test for bias and stereotype artifacts.

## 7) Glossary (terms that recur)
- **Generative reasonable person:** an LLM-based estimator of lay “reasonableness” judgments.
- **s‑RCT / Silicon Randomized Controlled Trial:** an RCT design where stateless model sessions are treated as experimental subjects.
- **Stateless session:** a fresh model interaction with no memory of previous vignettes.
- **Persona prompting:** asking the model to role-play a demographically specified respondent.
- **Sycophancy / demand effects:** the model aligning answers to perceived user intent rather than latent judgment.
- **Directional replication:** reproducing the sign/ordering of effects (structure), distinct from matching effect magnitude (calibration).
- **Lay formalism:** a pattern where perceived legal enforceability exceeds perceived consent and fairness in contract scenarios.

## 8) If you’re using this repo for RAG/training
Recommended “high-signal” files:
- Full text: `papers/ssrn-5377475/paper.txt`
- Primary summary: `papers/ssrn-5377475/summary.md`
- Chinese summary: `papers/ssrn-5377475/summary.zh.md`
- This study pack: `papers/ssrn-5377475/study_pack.md`

See also:
- JSONL exporter: `export_llm_dataset.py`
- Chunk builder (RAG): `build_rag_chunks.py`

"#;
pub const ARTICLE_TEXT: &str = r#"The Generative Reasonable Person
Yonathan A. Arbel
This Article introduces the generative reasonable person, a new tool for estimating how ordinary people judge reasonableness. As claims about AI capabilities often outpace evidence, the Article proceeds empirically: adapting randomized controlled trials to large language models, it replicates three published studies of lay judgment across negligence, consent, and contract interpretation, drawing on nearly 10,000 simulated decisions.
The findings reveal that modelscan replicate subtle patterns that run counter to textbook treatment.Like human subjects, models prioritize social conformity over cos-tbenefit analysis when assessing negligence, inverting the hierarchy that textbooks teach. They reproduce the paradox that material lies erode consent less than lies about a transaction's essence. And they track lay contract formalism, judging hidden fees more enforceable than fair.
For two centuries, scholars have debated whether the reasonable person is empirical or normative, majoritarian or aspirational. But much of this debate assumed a constraint that no longer holds: that lay judgments are expensive to surface, slow to collect,and unavailable at scale. Generative reasonable people loosen that constraint. They offer judges empirical checks on elite intuition, give resource-constrained litigants access to simulated jury feedback, and let regulators pilot-test public comprehensio,nall at a fraction of survey costs. The reasonable person standard has long functioned as a vessel for judicial intuition precisely because the empirical baseline was missing. With that baseline now available, departures from lay understanding become transaprent rather than hidden, a choice to be justified, not a fact to be assumed. Properly cabined, the generative reasonable person may become a dictionary for reasonableness judgments.



Professor of Law, University of Alabama, School of Law, Director AI Legal Studies. I

am grateful for the valuable feedback and insights provided by J. Shahar Dillbary, Niva E-lKkoinren, Andres

Swaicki, David A. Hoffman, Christopher Brett Jaeger, Ben McMichael, Peter N. Salib, Roseanna Sommers,

Kevin Tobia, Andrew Coan,and Matthew Tokson. This work also benefitefdrom comments byparticipants

in the NYU Empirical Contracts Workshop, Tel Aviv University Law & Tech Semina,rand faculty workshops

in Miami and Arizona Law. Justin Heydt and Andrew Robitaille provided important research assistance. Any

errors are my own.

ARBEL, THE GENERATIVE REASONABLE PERSON

2/51

I NTRODUCTION
In the contractschestnutLeonard v. Pepsico, thecase hinged on a single question: how woulpdeoplereasonably interprettaelevisionad offering prizes for Pepsi points, among them a fighter j?e1Jtudge Woodemerged fromher chambers with an answer: “no reasonable, objective person,” she wrote, “ would have understood the commercial to be an off.”e2r With that, she dismissedthe claim.
The case always stirs some controversy in the classroom. Bsuktyaourself not whether youagree withher decision, but rather what toolsdid the law give judges, alone in chambers, to read into the minds of the teenagers Peps’is ad targeted?Intuitive judgments are hard.Judges are rarely the target audience for the messages they are asked to interpret, anddevelopmental psychology and neuroscience suggest that adolescents are especially sensitive to s-oecvialuative and reward cues found in advertising . 3 Yet for centuries, determinations of reasonable interpretations havedemanded exactly this mind-reading. Critics charge that such unguided standardsrisk realizing legal realists’ greatest fear: a vessel for judicial bias masquerading as common se4nse.
For two centuries, we have lacked a cheap, scalable, empirical method for making lay reasonableness judgments legible to legal institut.i5oJnusry trials were designed to channel community standards, but they suffer from selection effects,
1Leonard v. PepsiCo, Inc., 88 F. Supp. 2d 116, 127 (S.D.N.Y. 1999), aff'd, 210 F.3d 88 (2d Cir. 2K0e0i0th).A. Rowley, You Asked for It, You Got It ... Toy Yoda: Practical Jokes, Prizes, and Contract Law, 3NEV. L.J. 526, 536 (2003) (finding that the case features in eight out of 15 sampled textbooks).
2 Id. at 131.
3See, e.g.,Laurence Steinberg,A Social Neuroscience Perspective on Adolescent R-iTskaking , 28D EV. R EV. 78, 79–86, 96–100 (2008); Sara-hJayne Blakemore,The Social Brain in Adolescence, 9 NAT . R EV. NEUROSCI . 267, 267–72 (2008) (reviewing developmental changes in soc-iaclognitive processing, including how social cues and social information are interpreted during adolescence); Peter H. Wright, Marian Friestad & David M. Boush, The Development of Marketplace Persuasion Knowledge in Children, Adolescents, and Young Adults, 24 J. PUB. POL ’Y & MKTG . 222, 222–27 (2005.)The FTC, for instance, consistently emphasizes that advertising to specific age groups should be evaluated based on different standards. See FTC Policy Statement on Deception, (Oct, 13 1983) (“When representations or sales practices are targetedcitfoicaauspdeience, such as children, the elderly, or the terminally ill, the Commission determines the effect of the practice on a reasonable member of that group.”); FTC, Guides Concerning the Use of Endorsements and Testimonials in Advertising, 87 Fed. Reg4. 4,288 (July 26, 2022) (request for comment) (“The Commission recognizes that it is difficult for children– especially younger children– to discern ads from entertainment.”)
4 The critical literature is vastS.ee e.g.,Leonardo J. B. Amorim,Reasonable Interpretation: A Radical Legal Realist Critique, 33INT 'L J. SEMIOT . L. 1043, 1056 (2020), (“radical realism allows the observer to notice that the appeal to “reasonability” functions as a joker in legal argumentation, a token allowing the interpreter and the public to cope with unconscious prejudgements, biases and exteprrneaslsures.”;)David Zaring, Rule by Reasonablene,s6s3ADMIN .L. R EV. 525, 552 (2011) (noting that “One strong critique of reasonableness review in administrative law is that it would allow judges to enact their political preferences); MAYO MORAN , R ETHINKING THE R EASONABLE PERSON : AN EGALITARIAN R ECONSTRUCTION OF THE OBJECTIVE STANDARD , 16-17 (2003)(“unsurprisingly, the reasonable person often turns out to bear a rather suspicious similarity to the judge.”); Jeffrey M. Hayes,oTRecuse or to Refuse: Se-lJfudging and the Reasonable Person Problem33, J. L EGAL PROF . 85, 88 (2008) (arguing that judges make determinations “with a concept of “reason” that is uniquely shaped by their own environment, which means that in practice th-ecsaolled objective reasonable person standard collapses into subjectivity.”); Audrey LC.erfoglio, Emily M. Petrie, Monica K. Miller, Is "Reasonable" Reasonable? A Content Analysis on Judges' Perceptions of the "Reasonable Person' St,a5n7dard UIC L. R EV. 743, 747 (2024) (emphasizing int-ejrudge variability due to differences life experienceSse)e; also Ain Simpson, Mark D. Alicke, Ellen Gordon & David Rose, The Reasonably Prudent Person, or Me?5,0J. APPLIED SOC . PSYCHOL . 313 (2020) (Finding that people rely more heavily on their own projected behavior than on "reasonably prudent person" standards when judging others' harmful actLiaownsr)e;nce Solan, Terri Rosenblatt & Daniel Osherson, Essay,False Consensus Bias in Contract Interpretatio,n108C OLUM . L. R EV. 1268, 1269 (2008)
5 Both statutory and constitutional analysis are beyond the scope of the analysis here, but it is worth noting the growing jurisprudential import of “ordinary” meaninSge. e Bostock v. Clayton Cnty., 140 S. Ct. 1731, 1738 (2020). See also Jesse M. Cross, The Fair Notice Fiction, 75 Ala. L. Rev. 487, 488 -9 (2023) (“the Court increasingly would prioritize a single concern: the original public meaning of statutes”)

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adversarial distortion, and inaccessibility for most disput6eSsu. rveys can measure
public perception, but they are costly, manipulable, slow, and remain underutilized.7 Mock juries and jury consultants are available only to wealthy litigants. 8 Some have responded by declaring the reasonable person a purely normative construct9, untethered from empiricalreality. But it is worth asking
how much of this is principled jurisprudence and how much icsoping with our lack of tools. 10At the end, in most cases all we haivsejudicial intuition, educated

6 Shamena Anwar, Patrick Bayer, & Randi Hjalmarsson,The Impact of Jury Race in Criminal Trials , 127

Q. J. ECON . 1017 (2012). The need for surveys was highlighted by Chief Justice Roberts’ comments during oral

argument in a statutory interpretation case: "[O]ur objective is to settle upon the most natural meaning of

the statutory language to an ordinary speaker nogf lEish, right? . . . So the most probably useful way of settling

all these questions would be to take a poll of 100 ordinary speakers of English and ask them what [the statute]

means, right?” Transcript of Oral Argument at 5-512, Facebook, Inc. v. Duguid1, 41 S. Ct. 1163 (2021) (No-. 19

511)C.ited in Kevin Tobia et. al.,Ordinary Meaning and Ordinary Peopl,e171U.PA. L. R EV. 365, 371 (2023).

7SeeOmri Ben-Shahar & Lior J. Strahilevitz,Interpreting Contracts via Surveys and Experimen,t9s2 N.Y.U.

L. Rev. 1753 (201. 7Se) e alsoThomas R. Lee & Stephen C. MouritsenJ,udging Ordinary Meaning, 127YALE L.J.

788 (2018). By one estimate, each survey costs the federal government $65,000, Federal Trade Commission,

Public Comment on Methodology and Research Design for Conducting a Study of the Effects of Credit Scores

and Credit-Based Insurance Scores on Availability and Affordability of Financial Products, 69 FED . R EG .

34,167 (June 18, 2004). Under the Paperwork Reduction Act, federal agencies must obtain approval from the

Office of Management and Budget (OMB) before collecting information from ten or more non -federal

persons, a process that requires internal agencyvireew, certification, and a 60-day public comment period,

often resulting in significant delays (44 U.S.C. §§ 3502(3), -33550067)S. ee alsoGisselle Bourns, Jennifer Nou &

Stuart Shapiro,Improving the Efficiency of the Paperwork Reduction A,cRtEG . R EV. (Oct. 30, 2018).

8 Industry estimates of costs range from a few thousand dollars for a minimalistic version to upward of

$50,000.SeeCasey Johnson,Focus Groups on a Shoestring Budge, tAitken, Aitken, & Cohn (Jul. 2, 2020),

https://www.aitkenlaw.com/fo-cgursoups-on-a-shoestring-budget ($8,000-$30,000); Merrie Pitera, What

Does a Mock Trial Cost, IMS (Sep. 30, 2021),https://imslegal.com/articles/w-hdaotes-a-mock-trial-cost (or

$10,000-$60,000); Andrew Guilford and Isabelle Ord,

Mocking Juries, 18 C AL . L IT . 1 (2005)

https://www.sheppardmullin.com/media/article/189_pub38(5“.apfdefw thousand dollars on statistical

information and jury consultation, or around $50,000 could be spent for a complete mock tr.ial.”)

9 Scholars debate whether the reasonable person standard is descriptive, prescriptive, or a hybrid of both.

The descriptive view treats reasonableness as reflecting actual societal norms. See In re Eastern Transp. Co.

(The T.J. Hooper), 60 F.2d 737, 740 (2dCir. 1932) (Hand, J.) (“In most cases reasonable prudence is in fact

common prudence; but strictly it is never its measure.”); Brian ShepparTdh,e Reasonableness Machin,e62

B.C. L. R EV. 2259, 2288 (2021) (discussing the “Average Conduct Conception” of reasonableness). The

prescriptive view argues that reasonableness is a normative ideal rather than an empirical observation. See

Gregory C. Keating, Reasonableness and Rationality in Negligence Theo,ry48 STAN . L. R EV. 311, 339 (1996)

(contending the standard embodies a community norm, not actual behavior); see aWls.oPAGE K EETON ET

AL ., PROSSER AND K EETON ON THE L AW OF T ORTS 175 (5th ed. 1984) (describing the reasonable person as “a

personification of a community ideal”). The hybrid approach recognizes both descriptive and prescriptive

elements. See Alan CalnanT, he Nature of Reasonablene,s1s05C ORNELL L. R EV. ONLINE 81, 83 (2020) (“Some

scholars say reasonableness is prescriptive, others say it reflects community values, and still others see it as a

mix of both.”); Kevin P. Tobia,How People Judge What Is Reasona,b7le0ALA .L. R EV. 293, 296 (2018) (arguing

reasonableness is “partly statistical and partly prescriptive”); Anita BernstTehine,Communities That Make

Standards of Care Possible, 77 C HI .-K ENT L. R EV. 735, 740 (2002) (noting the standard’s shifting balance

between objective and subjective approaches). Even in his otherwise strong critique of experimental

jurisprudence, Jimenez agrees that “some legal conce—ptssuch as reasonablenes—s invite or require the use

of lay understandings to determine at least part of their extension.” Felipe JimeSnoemz,e Doubts about Folk

Jurisprudence: The Case of Proximate CauseU, . C HI . L. R EV. ONLINE 1 (2021))

10 See Tobia, supra note 9, at 344 (highlighting the importance of understanding how ordinary people

generate reasonableness judgmentMs);arvin L. Astrada & Scott B. Astrada, Law, Continuity and Change:

Revisiting the Reasonable Person Within the Demographic, Sociocultural and Political Realities of the -Twenty

First Century , 14 R UTGERS J.L. & PUB. POL ’Y 200 (2017) (arguing that the rise in minority demographics

demands reassessment of the reasonable person standard); Francesco Parisi & Georg von Wangenheim,

Legislation and Countervailing Effects from Social Norm,sin EVOLUTION AND D ESIGN OF I NSTITUTIONS 25,

29–30 (Christian Schubert & Georg v. Wangenheim eds., 2006) ( summarizing empirical studies that show

that “[l]aws may more effectively influence behavioral outcomes when legal norms are aligned with the

existing social values”, whereas “[l]egitimaicsyundermined when the content of the law departs from social

norms”).

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guessing by precisely the elitecsriticized for being most removed from ordinary life.11
This Article providessuch a tool.It develops a methodology for eliciting reasonableness judgments from large language mode(lsLLMs) , called “Silicon Randomized Controlled Trials ” (s-RCT) .12 The approach treats independent model sessions as experimental subjects, leveraging their statelessness to enable controlled comparisons that isolate lay reasoning patterns from doctrinal recall or model sycophancy. It uses this methodology to successfully replicate with LLMs the results of human subject experiments.
Across three replications involving over 10,000 simulated judgments, leading models have internalized lay reasoning patterns that diverge from legal doctrine. Like human subjects, models prioritize social conformity over costbenefit analysis when assessing negligence. They replicate the paradox that material deceptions, though judged morime pactful to their receipient,sare seen aslesscorrosive of consent than deceptions about a transaction's essential nature. And they track lay “contract formalism,” perceiving hidden fees as more enforceable than fair, with judgments closer to ordinary consumers than to elite legal professionals.
Another finding is that m odels identify what tends to matter to ordinary people better than how much each factor matters. The numerical assessments that models give canmplify some effects and compress otheTrhs.is and other limitations serve as the backbone for the recommendations the Article develops, alongside the call for future research and experimentation.
Understandingwhy these results hold is a question worth pausing on, particularly for those drawn to the psychology of human judgment. Humans make reasonableness judgments through deeply intuitive "syste-m1" processes, relying on mental schemas developed over a lifetime ofsocialization. 13These frameworks are as hard to articulate as explaining how to tie shoelaces or hit a baseball.14 While this makes them illegible to formal legal analysis and simple algorithms alike, they are, ironically, especially accessible to LLMs: industria-l grade pattern detectors trained on the same human discourse from which these schemas emerge.
The theoretical and practicalapplicationsof this research are broad.15 Judges can check intuitions against simulated demographics before ruling on what "ordinary consumers" would understand. Regulators can p-itleostt whether

11See Koehn v. Delta Outsource Grp., Inc., 939 F.3d 863, 865 (7th Cir. 2019) (“One could say that, because Koehn did not read her employment contract, she never understood the terms and therefore was unable to assent to them.”);See also Solan et al., supranote4 (linking “ordinary meaning” claims to projection bias).
12All code and data used in this study are publicly available for replication, analysis, and follow-up work:https://github.com/yonathanarbel/genera-triveeasonable-person
13Linda Hamilton Krieger, The Content of Our Categories: A Cognitive Bias Approach to Discrimination and Equal Employment Opportunity, 47 STAN . L. R EV. 1161, 1165, 1190 (1995) (introducing schema theory in legal contexts), building on David E. Rumelhart, Schemata and the Cognitive System, in 1H ANDBOOK OF SOCIAL C OGNITION 71 (1984).
14See e.g.C, heng, P.W. & Holyoak, K.J., Pragmatic Reasoning Schema, 1s7C OGN .PSYCHOL . 391 (1985); Kevin J. Heller,The Cognitive Psychology of Circumstantial Eviden,c1e05 Mich. L. Rev. 241 (2006). My use of schema here is more narrow than its meaning in some of the social psychology literature; in my use, the focus is on latent mental models that are abstractive, but may be nonetheless complex (and even inconsistoefnetv),en more complex social phenomena.
15 See C alnan, supra note 9, at 82 (“Reasonable legal minds agree that reasonableness is one of the foundational concepts of American law, infiltrating everything from administrative, corporate, and constitutional law to crimes, torts, and contracts.”).

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proposed disclosures communicate effectively, at a fraction of survey costs1.6 Resource-constrained litigants gain access to rough proxies for mock juries previously available only to wealthy parties. In each case, the tool functions not as final arbiter but as empirical reference point: a dictionary for reasonableness that makes departures from lay understanding transparent rather than hidden.
The Article proceeds in four Parts. Part I explores the contested role and relevance of common reasonableness judgments in the adjudication of the reasonable personIt. presents recent advances brought about by the experimental jurisprudence literature.
Part II locates this investigation within the burgeoning “silicon sampling” literature in economics, sociology, and psychology, which studies the ability of LLMs to replicate survey results. 17Reviewing these developments, a recentSciencearticle arguedthat “LLMs might supplant human participants for data collection.”18 Though not without limitations , social scientists find the method attractive because it offers unique advantages in terms of cost, scale, and reproducibility, and may even reduce certain forms of bias compared to traditional approaches.
Part III is the empirical heart of the Article. It begins with a methodological puzzle: how can we test whether LLMs have truly internalized lay reasoning rather than merely memorized legal doctrinTeh?e problem is easy to overlook but difficult to solve . Models trained on vast legal corpora have encountered doctrinal definitions, case outcomes, and academic commentary. A model can recite the Hand formula or conclude that no reasonable person would view the Pepsi ad as serious, but this reveals nothing about whetherabitshoarbsed the schemas that actually drive human judgments. It is the difference between a student who memorizesthe answer key to an exam and one who learned the formulas.19Simply asking models to resolve cases or define reasonableness tests recall, not understanding.
The obvious remedy, presenting novel hypotheticals, introduces its own pathology. Models excel at detecting user intent and adjusting responses accordingly, a phenomenon researchers call "sycophanc2y0.I"f a prompt implies that two scenarios should be judged differently, the model may oblige regardless of its latent assessmenEt.ven presenting scenarios side by side cannot be trusted: the model can harmonize its answers to appear consistent. 21 Probing latent understanding requires controlling for both memorization and demand effects

16See Igor Grossmann et al.A, I and the Transformation of Social Science Researc,h380 Science 1108 (2023) (arguing LLM -based approaches can reduce cost and frictions of data collection while changing the research pipeline); Marko Sarstedt et aUl.,sing Large Language Models to Generate Silicon Samples in Consumer and Marketing Research: Challenges, Opportunities, and Guidelin, 4es1Psychol. & Mktg.21982 (2024) (discussing rapid, low-cost “silicon samples” as pretests, screeners, and iterative comprehension checks, with cautions)
17 SeeSarstedt et a.,l supra note16, https://onlinelibrary.wiley.com/doi/10.1002/mar.2. 1982 18 SeeGrossmann et a.l, supra note16. 19SeeRandall Balestriero, Jérôme Pesenti & Yann LeCunL,earning in High Dimension Always Amounts to Extrapolation, AR XIV (2021),https://arxiv.org/abs/2110.09; 4T8im5o Freiesleben & Thomas Grote, Beyond Generalization: A Theory of Robustness in Machine Learnin,g202SYNTHESE 109 (2023). 20 OpenAI, Sycophancy in GPT -4O: What Happened and What We’re Doing About It, https://openai.com/index/sycopha-nincy-gpt-4o/(Apr. 29, 2025) 21See OpenAI,Sycophancy in GPT-4o: What Happened and What We’re Doing About It (Apr. 29, 2025) (discussing “sycophancy” and contex-tdriven alignment with perceived user intent); Ricardo Domingue-z Olmedo, Moritz Hardt & Celestine Mendler-Dünner, Questioning the Survey Responses of Large Language Models, in NeurIPS 37 (2024) (showing sensitivity to question framing and prompt choices that can distort “survey-like” outputs); James Bisbee et al.S,ynthetic Replacements for Human Survey Data? The Perils of Large Language Models, Political Analysis (2024) (documenting instability and prompt dependence that undermines naïve survey substitution)

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simultaneously. Yet the standard tools for doing so in human research, random assignment and between-subjects comparison, seem unavailable when the "subject" is a single model that remembers what it said moments ago.
The key is to exploit a feature of LLM architecture usually viewed as a limitation: statelessne.ss22 When accessed through APIs rather than chat interfaces, each model session begins fresh with no memory of prior interactions. This transforms thousands of independent sessions into thousands of independent experimental subjects. Randomly assign conditioancsross sessions, and the model cannot harmonize, because no session knows what another session said. Present scenarios unlikely to appear in training data, and memorization cannot help. The result is Silicon Randomized Controlled Trials (S-RCTs): the methodological gold standardof social scienc,e23 adapted for AI. Part III develops this approach and applies it to three replications of human -subject studies spanning negligence, consent, and contract interpretation.
Part IV explores practical implicationsG. enerative reasonablepeople do not replace judgment; they check it. Had Judge Wood consulted a silicon study in chambers, she might have reconsidered her confidence that teenagers would see the ad as mere jest . 24 She could still override the model on normative or prudential grounds, but the tool would surface what intuition obscured. In this sense, the LLM functions like a dictionary for reasonableness judgments, much as courts consult dictionaries to discern ordinary meaning. Regulators could pilot-test public understanding of disclosures at a fraction of survey costs. Resource-constrained litigants could access rough proxies for mock juries. Legal scholars could validate folk jurisprudence claims empirically. In e ach case, generative reasonablepeople function not as final arbiters but as empirical reference points: a starting place for inquiry rather than its conclusion.
This Article extends a research program exploring how generative AI can surface social facts relevant to adjudication. Prior work with David Hoffman examined whether models could illuminate patterns of language use in contract communities.25This Article asks a related but distinct question: whether models can approximate the reasonableness judgme—ntasbout negligence, consent, and contractual meaning—that legal standards invoke but rarely measure.
In the background of this Article, a larger conversation looms: should we let AI enter into the business of judging humans? Scholars have strong views on this question. 26 This Article will not resolve it, but it will clear the precondition for having it, clarifying the qualitative threshold that LLMs meet.

22 For user applications, some AI providers now provide a memory feature, but this is not enabled in the current experiments.
23 L AWRENCE M. FRIEDMAN , C URT D. FURBERG , AND D AVID L. D EMETS , FUNDAMENTALS OF C LINICAL T RIALS , at v.(4th ed, 2010)
24 See generallyYonathan A. Arbel & Shmuel I. Becher, Contracts in the Age of Smart Readers, 90 GEO . WASH.L. R EV. 83 (2022). (showing that large language models, working as “smart readers,” can translate legal texts to be accessible to teenagers and translate among cultural dividSeese.)also Heinrich Peters & Sandra Matz, Large Language Models Can Infer Psychological Dispositions of Social Media U,s3erPsNAS Nexus e231 (2024), https://doi.org/10.1093/pnasnexus/p.g(afien2d3in1g that LLMs are especially capable of inferring psychological traits of younger individuals from their social media posts).
25On the use of LLMs as tools of interpretation, see Yonathan A. Arbel & David A. HoffmanG, enerative Interpretation, 99 N.Y.U. L. R EV. 451 (2024), recently discussed and adopted inSnell v. United Specialty Insurance Co., 102 F.4th 1208 (11th Cir. 2024)
26 SeeKiel Brennan-Marquez and Stephen HendersoAnr,tificial Intelligence and Role-Reversible Judgmen,t 109J. C RIM . L. & C RIMINOLOGY 137 (1019) (arguing that, independent of quality of decision, algorithms should not be in charge of adjudication based on —I believe—a quaint theory of role -reversibility as precondition to adjudication).

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It also underscores the urgency ohf aving it now, before practical compromises would force u.sAt the very least, the Article argues that, properly used, LLMs offer a sufficiently reliable improvement over current methods to justify careful integration into legal practice.

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1. FOLK OPINIONS AND THE L AW
How much should the law account for lay perceptions of reasonableness? The law has a basic duality. Judged by its mode of production, the law is clearly a formal, technocratic, and in some sense elitist enterpri2s7eI.t relies on a cadre of professional—s judges, legislators, regulato—rsto mediate its operatio.nIt follows specific rules that help shape its meaning, internal coherence, administration, and effectiveness. All of this involves jargon, terms of art, and specialized language:noscitur a sociis, habeas corpus, proximate ca.uOsen occasion, the law also cooptscommon terms such acsontract, tangible2,8 or fish.29
Yet to reduce law tothe sausage-making formalities of its production would distract from its practical ability to govern individuals that neither speak Latin nor particularly care about Hand’s formula. That is,hte law is also a social phenomenon, and it aims to speak in the language of the governed30.It is this social aspect that motivates many scholarly and reform proposals that push against the specialized language of the law.
Consider a few scholarly conversationtshat tap ordinary opinions. The technocraticplain language movement, the largest consumer reform movement of our generation, sought to rewrite the language of the law to match the common vernacula.r31A more scholarlyenterprise, thefolk jurisprudence proje,ct seeks to map the lay understanding of legal concepts and measure the divergence of lay and lawyerly understanding. 32 Likewise, a central method of legal interpretation, “ordinary meaning ” analysis, hews closely to lay usage of language.33Trends in criminal justice, perhaps most perniciously penal populism, seek to adjust sanctions to folk sense of desert and punishm34eInnt.contrast, the recent “lived experience” scholarship attempts to surface lay experiences of marginalized people into the study of the la3w5 .
The commitment to lay perceptions transcends the pragmatic and informs jurisprudence itselfF.or H.L.A Hart, lay intuitions form jurisprudence’s essence“:a general theory of law is just an attempt to elucidate the folk concept of law.”36Joseph Raz would later trace this lineage, arguing jurisprudence s“eoeukrs ordinary concept of law”— not as scholarsdefine it, but as bus drivers and IT
27 This is, in essence, the Hartian view of legal norms under the rule of recognition. For a fuller treatment, see Felipe JimenezL,egal Principles, Law, and Tradition, 33YALE J. L. & H UMAN. 59 (2020).
28C.R.S.A § 39-26-102 (2022) 15(b.5(I) (“"Tangible personal property" includes digital goods”). 29 See Yates v. United States, 574 U.S. 528 (2015) (discussing whether fish is a “tangible good” for purposes of section 1519 of the Sarba-nOexsley Act of 2002). 30T HE D ECLARATION OF INDEPENDENCE para. 2 (U.S. 1776) (“[T]o secure these rights, Governments are instituted among Men, deriving their just powers from ctohnesent of the governe”d). In an insightful article, Anya Bernstein argues against a narrow language of the governed view which she positions within an Austinian language-as-command jurisprudence, and suggests that the audiences of legal language are often government agencies. Anya Bernstein, Legal Corpus Linguistics and the Half -Empirical Attitude , 106 C ORNELL L. R EV. 1397, 1435 (2021). 31SeeYonathan A. Arbel,The Readability of Contracts: Big Data Analysis, SSRN. 32See generallyKevin Tobia, Experimental Jurisprudence, 89U.C HI . L. R EV. 735 (2022) 33 See e.g., Oliver Wendell Holmes, The Theory of Legal Interpretation , 12 H ARV . L. R EV. 417, 417 (1899)(“[W]e ask, not what this man meant, but what those words would mean in the mouth of a normal speaker of English...”); Richard S. Kay, Original Intention and Public Meaning in Constitutional Interpretation, 103NW. U. L. R EV. 703,719(2009) (“By definition, the public meaning of a rule is the one apparent to a competent speaker of the language from a mere inspection of the text.”). 34 SeeJocelyn Simonson,Police Reform Through a Power Len,s130YALE L.J. 778, 850 (2021). 35SeeRachel López, Participatory Law Scholarship, 123C OLUM . L. R EV. 1795 (2023). 36SeeBrian Leiter & Alex Langlinais, The Methodology of Legal Philosop,hiny T HE OXFORD H ANDBOOK OF PHILOSOPHICAL METHODOLOGY 467 (Herman Cappelen, Tamar Gendler & John Hawthorne eds., Oxford Univ. Press 2016. )

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professionalslive it.37Other scholars may espouse a more elitist or technocratic view of the law, but almost all agree that the law should be mindful of, if not always reducible to, lay attitude38s.
The evolution of the reasonable person concept in tort is illuminating. Making its debut appearance in 1837, the reasonable person was first conceived of as a“a man of ordinary prudenc”e3.9According to Rabin, that idea of fault in tort law was originally tied “to community expectations of reasonable behavior, rather than to the economist's perception of rational behavior. ” 40 That is, negligence standards were originally construed as anchored in empirical facts, in particular, exogenously determined community norm41s.It will not be until the Twentieth century that judges like Learned Hand would take a decidedly normative approach:
[I]n most cases reasonable prudence is in fact common prudence; but strictly it is never its measure. ... Courts must in the end say what is required; there are precautions so imperative that even their universal disregard will not excuse their omissio42n.
Today, these debates are still ongoing, with some taking a descriptive view, others prescriptive, and yet others, some hybrid of the 4t3wBou.t regardless of jurisprudential commitments, the concept of reasonablenessis never more than one degreeof separation from lay opinions4.4 This is for a combination of descriptive, pragmatic, and normativreasons. The firstreasonis reflective: when we know what lay people truly think, we gain a better understanding of what legal concepts mean4.5 The second is effectiveness: if the law sets to direct behavior, it should speak in the language of the governed, or at least be attuned to how it is being heard. This is part of the animus of the ordinary meaning interpretive theory.46 The third is legitimacy: for people to trust the law, they should be able to understand it.47 This is closely related to participatory arguments, about the public’s role in shaping their lives. The fourth is political. If the publiics to set a check on the operation of the legal systemit,is important that it understand its laws, commands, and boundarie.4s8
37SeeJOSEPH R AZ , PRACTICAL R EASON AND NORMS 164 (2d ed. 1999C)i.ted in Jimenez, supra note9 38 See Jimenez, supranote9. 39Vaughn v. Menlove (1837) 132 ER 490 (CP). 40 See Robert L. Rabin, The Historical Development of the Fault Principle: A Reinterpretation, 15GA . L. R EV. 925, 931 (1981) 41For an articulation of the positivist view,seeAlan D. Miller & Ronen Perry, The Reasonable Perso,n87 N.Y.U.L. R EV. 323, 37-02 (2012). 42 The T.J. Hooper, 60 F.2d 737, 740 (1932) at 740. 43 Lucien Baumgartner & Markus Kneer, The Meaning of “Reasonable”: Evidence From a Corpus Linguistic Study 1–2 (Aug. 29, 2023), https://ssrn.com/abstract=4555547; Benjamin C. Zipursky, Reasonableness in and out of Negligence Law, 163 U. Pa. L. Rev. 21310, (2210515) (proposing a hybrid view). 44 See also Baumgartner & Kneer, suprnaote43, at 4 (“there are grounds for (considerable) correspondence between the lay concept of reasonableness and its legal equivalent”). 45 See Tobia, supra note 32, at 750 (Presenting “the “fol-klaw thesis.” . . . this account would predict that the legal concept of causation reflects features of the ordinary concept of causation and that the legal concept of consent reflects features of the ordinary concept ofsceonnt.”). 46 See Tobia et al., supra note 6, at 372 (noting the motivations behind modern textualism include “concern for democracy, fair notice, or rule of law values, or objective inquiry into meaning”). 47 TOM T YLER , WHY D O PEOPLE OBEY THE L AW (2006) at 7 (if people “regard legal authorities as more legitimate, they are less likely to break any laws . . . A normative perspective leads to a focus on people’s internalized norms of justice and obligation. It suggests the need to explore what citizens think and to understand their values”) 48 Jason M. Solomon,The Political Puzzle of the Civil L Jury , 61EMORY L.J. 1331, 1340 (2012) (“Historically, the civil jury in the United States, like the criminal jury, was justified in large part as a check against the abuse of government power.”

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There is also the more Hayekian reason .49 Disperse individuals have access to information not available to the central planner. Lived experience and peer-to-peer interactions produce perspectives and knowledge that arlegnibolte to either well-meaning policymakers or well-read scholars. Aggregating this information leveragesthe wisdom of the crowdsp,otentially creating judgments more accurate thanthat ofany specific individual5.0
Thus, it may be the case that the more modern tort view is that the reasonable person is essentially normative creation,a man fashioned in the judge’s image.51Yet, even if we are all realists nowa,nd even if we all understand nowthe reasonable person as a normative const,ruitcits still incumbentuponon us to reflect on how thatjudicial personrelates to actual peopleT.he descriptive neednot determine the normativ;ebut it caninform it.52
But this conclusion presents the challenge: how can the State make ordinary opinions legible to itself?53How can the law discover what lay people think? And in a democracy, wherethe people rul,ehow dowe know what people think?

49 Friedrich A. Hayek, The Use of Knowledge in Society, 35AM. ECON . R EV. 519 (1945) (“The knowledge of the circumstances of which we must make use never exists in concentrated or integrated form, but solely as the dispersed bits of incomplete and frequently contradictory knowledge which all the separate individuals possess.”).
50 A recent prediction competition is illustrative of this phenomenon. Asked to predict multiple future events, the average participant (N=3,300) ranked slightly worse than chance. The average aggregate prediction, however, ranked at the t9h5percentile of all participants. https://www.astralcodexten.com/p- /who predicted- 2023.
51See e.g., Mayo Moran,The Reasonable Person: A Conceptual Biography in Comparative Perspecti,ve14 L EWIS & C LARK L. R EV. 1234, 1236 (2010) (“both in the context of the law of negligence and in the criminal context, the objective content of the reasonable person is closely linked to standards of ordinariness or normalcy”).
52ee Brian Z. TamanahaB, eyond the Formalis-tRealist Divide: The Role of Politics in Judgin(g2010) (arguing realism’s insights have become part of mainstream judging debates); Brian LeiLterg,al Realism and Legal Positivism Reconsidere,d111Ethics 278 (2001) (describing realism’s enduring influence on contemporary legal theory)
53On legibility as a central goal of the state, seJAeMES C. SCOTT , SEEING LIKE A STATE (1998)

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2. GENERATIVE PEOPLE IN T HEORY AND THE SOCIAL SCIENCES
LLMs are, at core, industrial -grade pattern recognizers.54 The idea behind generativereasonable people is that, because of this, they might also pick up on the subtle, complex, and perhaps even-csoenlftradictory patterns that drive the everyday judgments of individuals on matters of reasonable.5n5ess
This claim may seem ambitious at first. Legal scholars are already familiar with the effectiveness of statistical models to predict case outc5o6mFoers. instance, even though Supreme Court cases are involved and compleax,single factor—political affiliation of the Justice’s nominating Presiden—t will be very predictive of outcomes in political case.5s7The literature on structural bias also shows that raceand socioeconomic status alshoold predictive power, even if they really should not. 58 Similarly, machine learning models excel at predicting consumer behavior in advertising and fraud detection. 59 Yet, predicting reasonableness judgmen,tswhich appear soclosely related toethical and social judgments, mayseemqualitatively different. Why should we expect generative AI models to achieve even modest predictive accuracy on nuanced questions of reasonableness?
This Part offers the theoretical and empirical background necessary to assess this possibility. The first section explores how modern LL’Masrchitecture and emergent capabilitie,ssuch as attention mechanisms, roleplaying abilities, and generalization, make them potentially suitable for simulating ordinary people's judgments. These same architectural features, however, also introduce systematic limitations: a majoritarian bent that may marginalize minority perspectives, susceptibility to bias amplificoant,iand “value drift” as social norms evolve beyond the models' training data. The second section reviews empirical evidence from the burgeoning “silicon sampling ” literature in psychology, economics, and sociology, which demonstrates that LLMs can replicate human survey responses across diverse domains with surprising fidelity. Taken together, these developments suggest cautious optimisgme:nerativereasonable people are theoretically plausible and empirically promising, but their deployment demands

54 For more on the distinction between simulation and prediction, see Yonathan A. ATrbimele, & Contract

Interpretation: Lessons from Machine Learnin,gin R ESEARCH H ANDBOOK ON L AW AND T IME (Frank Fagan

& Saul Levmore eds., forthcoming 2024).

55This fallacy, that AI analysis constitutes a form of judgment rather than pattern recognition, has led to

some confused commentary that this Article decidedly avoids. See Brennan-Marquez & Henderson,

Artificial Intelligence and Role-Reversible Judgmen,t109J. C RIM . L. & C RIMINOLOGY ARt. 1 (2019).

56See, e.g.,Theodore W. Ruger et al.T,he Supreme Court Forecasting Project: Legal

and Political Science Approaches to predicting Supreme Court Decision Mak,in1g04C OLUM . L. R EV., 115–0

1210 (2004) (predicting, with 76% accuracy, case outcomes based on sparse factors). Kimo Gandall, Chris

Haley, Juliana Chhouk, Logan Knight, Alex Wang, and Bella DeMarco,

Predicting Precedent: A

Psycholinguistic Artificial Intelligence in the Supreme Cou,r1t4 220 (2023) (offering a modest improvement,

but at the cost of a complex model).

57SeeJeffrey A. Segal & Alan J. Champlin,The Attitudinal Model, in R OUTLEDGE H ANDBOOK OF JUDICIAL

BEHAVIOR 29 (2017) (“The attitudinal model is the most dominant model for understanding the Supreme

Court’s decisions on the merits. In fact, for the eight justices currently on the Court prior to the 2016

term, the correlation between their ideologaynd their voting behavior on the Court is a .94”).

58U.S. Sentencing Comm’n,Demographic Differences in Federal Sentencing: An Update to the 2012 Booker

Report(2023) (finding Black male offenders receive on average 13.4% longer sentences than comparable White

males); Will Dobbie, Jacob Goldin & Crystal S. YangT, he Effects of Pretrial Detention on Conviction, Future

Crime, and Employment, 108AM. ECON . R EV. 201, 20–304 (2018) (causal evidence that inability to pay bail

increases conviction probability and worsens outcomes).

59SeeJohn Ford et al.,AI Advertising: An Overview and Guidelines, J. BUS. R SCH . 166(2023).

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careful attention to their inherent limitations. As to whether generative reasonable people work in practiciseaddressed empirically in the next Pa6rt0.
1. Generative AI andSilicon Jurors: Emergent Capabilities
The foundation ofgenerative reasonablpeeople lies in the sophisticated architecture of modern artificial intelligence systems, particularly in the realm of natural language processing61.Four key capabilitiesmake them suitable for the task of modeling reasonableness judgemen:tasttention mechanisms, emergent roleplayingcapabilities, generalizationabilities, and their “majoritarian bent”.62
Current generative AI architectures, including those fit for generative reasonable people, rely on autoregressive mod63eTlsh.ese models generate output sequentially, with each token(roughly, a word)conditioned on those previously generated6.4 During training, the models are fed large volumes of data—more text than any human can read in a lifetim—e and are tasked with predicting the next token in a sequence of words. The model learns by minimizing prediction errors, gradually improving its ability to anticipate what comes next in human language.
The true breakthrough that catapulted language models to their current capabilities was the introduction of the transformer architectu65rAe.t its heart is the attention mechanism, which allows the model to dynamically weigh the importance of different parts of the input. 66 In this architecture, the values assigned to the vector representation of each token are adjusted based on contextual relationships. A token like “sea” would be described by various numbers, indicating its relationship to concepts like water, ships, and Poseidon. These numbers adjust based on context: desalination, circumnavigation, or Odysseus' travails.
The importance of this becomes apparent when we consider the word 'bank.' On its own, it is ambiguous: a financial institution or the side of a river? But when humans see a sentence like“Frank needed money so he went to the bank”, they immediately adjust their understanding of the word based on context. So does the model; the attention mechanism shifts the meaning of “bank” towards financial institution when it encounters the word 'money' in this sentence. Just as a judge might focus on key elements oaf saecwhile considering the broader context, AI attention mechanisms allow models to prioritize relevant information when making judgments - crucial for assessing reasonableness in complex legal scenarios.
One of the most fascinating aspects of these models is theiremergent capabilities. An emergent property is one that appears only at a given level of

60 See infra Part III.

61At this point, there is no dearth of introductory materials at different levels of technical expertise. For an

overview of the rapid improvements in the field, sAejei Supriyono, Aji Prasetya Wibawa, Suyono & Fachrul

Kurniawan,Advancements in Natural Language Processing: Implications, Challenges, and Future Direc,tions

16T ELEMAT . & INFORMAT . R EP. 100173 (2024)

62 Yonathan A. Arbel & David A. Hoffman, Generative Interpretation, 99 N.Y.U. L. Rev. 451, –48736 (2024).

63For an introduction geared towards lawyerse, e Arbel & Hoffman, supra note62, at 476–83.

64 tokens are commonly appearing word subparts, such as ‘th’ in English . A helpful list of all the 100,00

tokens used by GPT

-4 can be found here:

https://gist.github.com-/ms acke/ae83f6afb89794350f8d9a1ad8a09193.

65SeeAshish Vaswani et al.A, ttention Is All You Need, arXiv:1706.03762 (2017).

66 The earlier models did not use attention mechanisms, but given the dominance of transformers today, I

focus on them.

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complexity.67 The ability to roleplay character is an emergent property and modern models perform well on this task.68 No programmer explicitly coded rules for this behavior- rather, roleplaying emerges naturally from the system's fundamental function of nex-ttoken prediction. If a sentence mentions that the speaker is Chris Tarrant, the probability shifts toward predictni g “Is that your final answer” as the next phrase. Context affects prediction, and identity informs context.69
Roleplaying is crucial for generativereasonable people. This capability allows models to produce responses that cohere with broad patterns of reasoning among common people and shift from “expert” mode to layperson mode. It instructs the model to move from its default“,helpful assistan”t voice, to more realistic depictions of ordinary people70. This transition is essential if we seek a non-elitist notion of reasonableness, as models have internalized both expert and lay patterns of reasoning.
Generalization is another crucial emergent property of these systems. A model like LLaMA -3 has 70 billion parameters but it is trained on 15 trillion tokens.71The ratio is one parameter for every 214 tokens. This means that rote memorization of all the data the model sees during training is impossible. Instead, the model must learn to compress the information by generalizing the patterns it sees. This is similar to how humans learn abstract rules rather than memorizing the details of every specific instance. We might not remember each cat we have seen, but we learn to identify them by generalizing the concept of a cat from its specific instances. In other wordsw, e develop a model o“fcatness”, and while we will be hard-pressed to articulate it, it allows us to quickly and efficiently identify cats even in novel situation72s.
Generalization is vital for generativereasonable people because many questions will involve scenarios different from those in the training data. The hope is that models have generalized ideas about reasonableness rather than simply memorizing specific instances when an act was deemed reasonalbe or unreasonable7.3While generalization is necessary, it doesn't guarantee suc-ceitss may be superficial, crude, or mistaken. What matters is that AI models can develop complex internal models beyond simple pattern recognition.
Even though generalization may be necessary to the task at hand, it doesn’t guarantee success. Generalization may also be superficial (overfitting),

67 See Yonathan A. Arbel, Reputation Failure: The Limits of Market Discipline in Consumer Markets, 54

WAKE FOREST L. R EV. 1239, 1252 n. 64 (2019).

68 SeeZekun Moore Wang et al.R, oleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities

of Large Language Mode,lsAR XIV preprint (2024)h, ttps://arxiv.org/abs/2310.00(7“4S6tate-of-the-art (SOTA)

LLMs like GPT -4 . . . exhibit advanced ro-leplaying capabilities”); Keming Lu et al.L,arge Language Models

are Superpositions of All Characters: Attaining Arbitrary Role -play via Self -Alignment, arXiv preprint

(2024), https://arxiv.org/abs/2401.124(7“4GPT -4 has already demonstrated outstanding role -playing

abilities”); Jiangjie Chen et al.F,rom Persona to Personalization: A Survey on R-oPlelaying Language Agent,s

AR XIV preprint (2024),https://arxiv.org/abs/2404.18(2“3P1ersonas are inherent in LLMs, and role-playing

them capitalizes on the statistical stereotypes in LLMs”).

69 Another technical aspect that contributes to the success of roleplaying activities is instru-ctuionning of

models, which improves their ability to stay in characteSre. e Chen et al., supranote68.

70 See Lu et al., supra note 68 (positing that roleplaying arises from a combination of training data and

in-context learning).

71SeeMeta,Introducing Meta Llama 3: The Most Capable Openly Available LLM to Date, Meta AI (Apr.

18, 2024), https://ai.meta.com/blog/m-lelatma a-3/.

72 Reddit, "r/CatsInWeirdPlaces," https://www.reddit.com/r/CatsInWeirdPlaces/.

73The phenomenon of generalization is also known as “grokking” and the study of the points in training

where models “grok” new concepts is an active area of research. See e.g., Hu Qiye, Zhou Hao & Yu RuoXi,

Exploring Grokking: Experimental and Mechanistic Investigations

, AR XIV:2412.10898(2024),

https://arxiv.org/abs/2412.10. 898

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crude (underfitting), or simply mistaken. This means that we would want to test both the existence of a generalized modealnd its adequacy. But the key for now is to understand that AI models can learn more than simple patterns in data, and they can develop internal models that are more complex. (in fact, many complaints about algorithmic black boxes show that these internal molds emay be too complex).
A final intriguing characteristic is what we might term their “majoritarian bent.” 74 Models favor broader patterns over narrower ones, manifesting as a pro-majority bias. This arises from two factors: the statistical nature of next-token prediction inherently favors common patterns, and po-st training adaptations like Reinforcement Learnin g from Human Feedback further align models with general human preferences. This majoritarian tendency makes these models w-eslul ited for simulating the“reasonable perso”n standard, as they naturally gravitate toward common opinions and wid-ehlyeld beliefs7. 5
While the majoritarian bent is essential to the utility of generative reasonablepeople, it also pointso the limits of this techniqueT.he models mirror aggregated human knowledge and biases, including problematic on7e6 Isn. legal contexts, they may struggle with cases requiring consideration of diverse or minority perspective-s a concern highlighted by critical scholars. Feminist legal theorists have exposed how the supposedly neu“treaal sonable perso”nhas often been the “reasonable man” in practice, with majoritarian defaults imposing asymmetric standards that present majority experiences as the natural bas7e7line. Generative reasonablpeeople may well replicate such patter7n8s.
Two other limitations deserve emphasis. First, general models struggle to reliably simulate specific individuals - they face a “granularity problem.”79 While some applications attempt to emulate specific people through-ftiunneing and context learning,80 the effectiveness of such approaches remains unproven. Second, pretrained models have a limited “half-life” as social norms and perceptions change, creatin“gvalue drift” that makes older models incapable of reflecting societal evolution8.1
To summarize, there is a surprising but deep connection between modern AI architecture and generative reasonablepeople. These models can understand context, adopt various perspectives, generalize from examples, and reflect common social norms. However, they also have notable limitations

74 See Arbel & Hoffman, supra note 62.
75 Id.
76 See generallySandra G. Mayson,Bias In, Bias Out, 128YALE L.J. 2122(2019). 77 SeeSusan Dimock, Reasonable Women in the Law, 11.2C RIT . R EV. INT 'L SOC . & POL . PHIL . 153, 153 (2008) (“What counts as reasonable in these and many other areas of law is typically conceptualized against a ‘reasonable man’ . . . standard”). Dimock argues that eve-nlelvoewl er abstractions, like ‘reasonable woman’, are still o-vgeerneralized. 78 Psychometric analysis suggests that “LLMs exhibit a tendency toward Undifferentiated, with a slight inclination toward Masculinity.” Jen-tse Huang et al.,On the Humanity of Conversational AI: Evaluating the Psychological Portrayal of LLM,sin Proc. of the Int’l Conf. on Learning Representations (ICLR) (2024). 79 See Wang et al., supranote68. 80 For an excellent review of the literature studying the differences between parametric a-npdanraomn etric roleplaying, See Chen et al., supranote68, https://arxiv.org/abs/2404.1.8231 81A feature and a bug: having time frozen models can also be useful in depicting the attitudes of older generations. Perhaps some arguments about originalism could have been resolved had we had a powerful model trained on materials from that time.

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regarding bias, individuality, and temporal relevanc-econsiderations that must inform their application to legal questions.
2. Silicon Sampling in Social Sciences
A growing body of research on“silicon sampling” demonstrates LLMs' ability to provide human-like responses across various domains of social science. It has led to some interesting discoveries on the power of AI to provide hum- an like feedback in various areas of the social sciencaensd adds to the plausibility of generativereasonable people82.
The evidence is striking. One study found that LLMs can generate moral judgments highly correlated (r=0.95) with human judgmen8t3sA. nother showed that on eleven sociological questions, LLM responses closely aligned with those of the general population.84 Early versions of ChatGPT successfully replicated multiple psychological studies8.5Another psychological study foundthat LLMs can persuasively assume big five personality traits such as extroversion or agreeablenes8s6.perhaps most impressively, a recent study found t“hCahtatGPT 4 exhibits behavioral and personality traits that are statistically indistinguishable from a random human from tens of thousands of human subjects from more than 50 countries”.87
LLMs even replicate human cognitive biases, bringing them closer to actual human judgment8.8 For example, one study found that LLMs can recreate classic findings in economics and psychology, such as the ultimatum game and the Milgram obedience experiment8.9 Interestingly, models sometimes display more ethical behavior than human-sshowing less selfishness and greater fairness toward out-group members, raising questions about whether we want perfect mimesis or idealized behavio9r0.
The roleplaying capabilities of LLMs offer particularly promising applications for legal analysis. Models can be prompted to respond as reasonable persons from various demographic backgrounds or even to simulate the

82See e.g.,See Sarstedt et al., supranote16. 83See Danica Dillion, Niket Tandon, Yuling Gu & Kurt Gray, Can AI Language Models Replace Human Participants?28 T RENDS C OGN . SCI . 597 (2023). In hindsight, it is not entirely surprising because these methods are trained to mimic human moral judgments using RLHF and similar techniques. 84 See James Bisbee et al.S,ynthetic Replacements for Human Survey Data? The Perils of Large Language Models, in Political Analysis (Published online 2024:1-16.) doi:10.1017/pan.2024.5 The researchers find low accuracy regarding the distribution of synthetic opinions, a point we revisit later. 85 See Peter S. Park et al. Diminished diversity‑of‑thought in a standard large language model , arXiv:2207.07051 (2023). 86 SeeHang Jiang, Xiajie Zhang, Xubo Cao, Cynthia Breazeal, & Jad Kabbara,PersonaLLM: Investigating the Ability of Large Language Models to Express Big Five Personality Traits , arXiv:2305.02547v5 (2023), https://doi.org/10.48550/arXiv.2305.02547. 87Qiaozhu Mei, Yutong Xie, Walter Yuan, & Matthew O. JacksonA, Turing Test of Whether AI Chatbots Are Behaviorally Similar to Humans, [Small Caps]Proc. Nat'l Acad. Sci. U.S.A.[/Small Caps], Feb. 22, 2024, at e23139251h2t1tp, s://doi.org/10.1073/pnas.2313. 925121 88 See Andrew K. Lampinen et al., Language Models Show Human-Like Content Effects on Reasoning Tasks, arXiv:2207.07051 (2022), https://doi.org/10.48550/arXiv.2207.07051 (showing that the framing of questions misleads humans and LLMs in similar ways);See also Park et al., supra note 85(showing false consensus bias). 89 Gati Aher, Rosa I. Arriaga & Adam T. Kalai, "Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies,"Pinroc. of the 40th Int’l Conf. on Machine Learning(ICML 2023) 90 See Huang et al., supra note78(finding that “LLMs demonstrate reduced ICB scores compared to the general human population.” The ICB scale is a measure of an “individual's belief in whether their ethnic culture predominantly shapes a person's identity”).

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reasoning of historical legal figureTs.his capability has proven so compelling that
companies like Character.ai have built billion -dollar companies around it, offering interactive experiences with simulated person91as.
But how accurate are these simulationIsn?one validation study using a
“personal Turing tes,t” AI models imitating specific individuals achieved a 48.3% success rate in deceiving acquaintances of those individuals . 92 This is quite
remarkable: in half of the cases, acquaintances could not tell apart a model from the actual person93.
Research shows these capabilities can be enhanced through various techniques. 94 Giving personas demographically typical names improves performance.95One study promptedLLMs to assume the persona of people with
specific demographic characteristics and answer a few questions. Then they asked
humans to answer the same questions, some of them met these demograp-hics (in
group), some of them assumed the persona of that person (out-group). T he
researchers find that in some instances, LLMs sound more like ou-gt roup than in- group members9.6 This capability is improved if the model is given a name that is consistent with the underlying demographi9c7.Further, injecting randomness
into responses helps prevent “group flattening” - the tendency to produce stereotypical answers for minority grou.9p8s
This is consistent with another recent research paper that foutnhdat in
situations where humans have polarized views, persona assignment helps the model express differing view9s9.Finally, and in the other direction, a recent study
suggests that many results that align with specific demographic merely reflect prompting effects and disappear when prompts are carefully ve1t0t0ed.
While the focus here is on thgeenerative reasonablpeerson,the research
also points toward the possibility ofgenerativejuries. Studies show that when
LLM agents interact, they exhibit group dynamics similar to human collectives,

91Character.AI In Early Talks for Funding at More Than $5 Billion Valuation, BLOOMBERG (September 28, 2023, 4:43 PM CDT) https://www.bloomberg.com/news/articles/-20092-238/characte-rai-in-early-talks-forfunding-at-more-than-5-billion -valuation
92See Man Tik Ng et al., How Well can LLMs Echo Us? Evaluating AI Chatbots' Role -Play Ability with ECHO , arXiv:2404.13957 [cs.CL] at 7 (Apr. 22, 2024), https://arxiv.org/abs/2404.13957.
93See Man Tik Ng et al., How Well Can LLMs Echo Us? Evaluating AI Chatbots’ Role -Play Ability with ECHO (2024) (reporting “personal Turing test” results measuring whether acquaintances can distinguish chatbot role-play from the person); Qiaozhu Mei et al., A Turing Test of Whether AI Chatbots Are Behaviorally Similar to Humans, 121(9)Proc. Nat’l Acad. Sci. e2313925121 (2024) (c-rnoastsional behavioral similarity tests and distributions).
94 See e.g.,Cheng Li et al., RoleLLM: Benchmarking, Eliciting, and Enhancing Role -Playing in Large Language Models, arXiv:2310.00746 (2023)h, ttps://arxiv.org/abs/2310.007A4li6reza Salemi et al., LaMP: When Large Language Models Meet Personalization, arXiv:2304.11406 (2023), https://arxiv.org/abs/2304.11;4N0il6imesh Halder, Harnessing the Power of Role-Playing in Advanced AI Language Models: A Comprehensive Guide to ChatGPT’s Potenti,aMl edium (2023),
95Angelina Wang, Jamie Morgenstern & John P. DickersonA,rtificial Intelligence Chatbots Mimic Human Collective Behaviou,rarXiv:2402.01908v1 [cs.CY] (Feb. 2, 2024), https://arxiv.org/abs/2402.01908v1.
96 Id. 97 Id at figure 3. 98 Id. 99 Tiancheng Hu & Nigel Collier, Quantifying the Persona Effect in LLM Simulations , arXiv:2402.10811 (Feb. 26, 2024), https://arxiv.org/abs/2402.10811. 100 Ricardo Dominguez -Olmedo, Moritz Hardt & Celestine Mendler -Dünner, Questioning the Survey Responses of Large Language Mod,einls ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 37 (2024).

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modeling complex social phenomena like bank run1s0,1realistic macroeconomic phenomena1,02information cascades1,03and community formation.104
Of course, limitations remain.105 Quality roleplaying requires sufficient contextual information about the individuals or groups being simulated. Current models struggle with some types of reasoning, particularly around politically charged topics.Prompting models to act as a typical member of a group risks reinforcing simplistic or stereotypical portrayals of complex groupAsn.d ethical concerns about“speaking for” practices must be addresse1d06,particularly when simulating marginalized groups.
Despite these challenges, the silicon sampling literature provides substantial evidence that LLMs can approximate huma-nlike judgments across diverse domains. This capability forms the foundation for our exploration of generative reasonablpeeople in legal contexts.

101Sophia Kazinnika, Bank Run, Interrupted: Modeling Deposit Withdrawals with Generative A,IFederal

Reserve Bank

- Quantitative Supervision & Research (Oct. 30, 2023),

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4656722.

102Ningyuan Li, Chong Gao, Yiming Li, & Qi Liao, Large Language Mode-lEmpowered Agents for Simulating Macroeconomic Activitie, sarXiv:2310.10436 (2023).

103 See Huang et al., supranote78.

104 James He et al.A, rtificial Intelligence Chatbots Mimic Human Collective Behaviou, rpreprint at Research Square (2024).

105 See supra note 86 and accompanying text. 106Linda Martín Alcoff, The Problem of Speaking for Other,sC ULTURAL C RITIQUE , No. 20, 5–32 (1991).

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3. T HE R EASONABLE PERSON IN SILICON
This Part offers an empirical assessment goefnerative reasonablpeeople. As noted in the Introduction,evaluating models’ deeper latent understanding of concepts runs the risk of learnin-tgo-the-test, contamination, and sycophanc1y07. To address these issu,etshe methodologydeveloped, -SRCT, exploits the stateless nature of large language models and uses them to adathpet standard method of randomized controlled trials. By testing models under controlled conditions mirroring human studies, we can probe their latent reasoning, not just their ability to parrot precedent.

1. The Core Methodology: S-RCT
The foundational challenge confronting any attempt to use AI as an instrument of applied jurisprudence is methodological: How can we know what LLMs truly understand about reasonableness rather than what they have memorized?
Two distinct threats to validity confrontnaïve attempts to answer this question. The first is “learning to the test” or “contamination”. Models trained on legal corpora may have encountered doctrinal definitions, case outcomes, or even the academic studies we seek to replicate. Asking a model to define reasonableness or resolvLeonard v. Pepsicois a test of recalln, ot generalization. The models are very likely to succeed at such tasks, but this will not answer the question.
The second threat is demand effects or “sycophancy.” To solve the previous issue, we might present models with unsheyepnotheticalsand than assess their responses. The problem is that models excel at inferring user intent and adjusting responses accordinglyT.hus, the models might give the right answer not because it learned that a factor is important but because the question structure might imply that. Even putting side-by-side scenarios will not resolve the issue. If a model sees two scenarios side bdyes,i it may infer that we expect differential treatment and harmonize its answers to appear consistent. To probe a model's true latent representations, we need methods that control both threats simultaneously.
The solution emerges from an architectural feature of modern LLMs typically viewed as a limitation: their fundamental statelessness. When interacting with LLMs through APIs rather than standard chat interfaces, each conversation starts from a blank slateitwh no memory of prior interactions. This creates an unexpected experimental opportunity. We can treat each independent model session as a distinct experimental subject, enabling adaptation of the gold standard of causal inference to the study of AI reasoning: silicon randomized controlled trials (S-RCT).
S-RCT rests on three mechanisms. The first is statistical independence through statelessness. Thousands of separate model sessions receive random assignment to experimental conditions with no ability to communicate or coordinate responses. A session assigdneto one condition cannot observe, and therefore cannot harmonize with, sessions assigned to other conditions.
107See supra Introduction.

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The second mechanism is the shift from absolute to differential measurement. Rather than asking models to produce judgments vulnerable to memorization and calibration problems, we measure how responses change between randomly assigned conditions. Even imf aodel has learned from case law that "reasonable care requires salting icy sidewalks," it cannot anticipate our specific experimental manipulation or know what score another session produced under different conditions. The differential approach isolates t he model's sensitivity to manipulated factors from any memorized baseline.
We say a model isaligned to human judgment ifit shifts its judgments in the same direction humans do when experimental parameters vary. This sidesteps the problem of comparing raw scores across different types of reasoners. We need not ask whether a model's "6 out of 10" means the same as a huWmean's. ask only whether both shift upward when a precaution becomes more common, and whether that shift is statistically reliable.
The third mechanism is persona assignment. Models are instructed to roleplay demographically specified individuals, injecting realism and variation into the decision process. For robustness, the studies here test whether personas improve alignment with hum an baselines and verify results across multiple models: GPT-5 Chat, DeepSeek R1, Grok-4-Fast, Claude Sonnet 4.5, Gemini 2.5 Pro, Kimi K2, Qwen 3 maxa, nd Mistral Medium. This selection spans op- seonurce and proprietary architectures, different training app roaches, and varying parameter counts, offering an overview of today's state of the art.
Sample Persona Instructions You are roleplaying as Mary Alvarez. Alvarez is a 61 year old Hispanic woman. Politically, she is Lean Democrat. Personality description: Mary Alvarez, at 61 with a high school education, exhibits a down-to-earth yet pragmatic persona. Her modest yet comfortable lifestyle as an emoypel d individual within the $75-K $150K income bracket reflects her practicality and steadiness. As a homeowner with a Mainline Protestant faith and leaning Democrat voter stance, she likely possesses traditional values with a touch of progressive thoughtnfeusls. Married without children, Mary may be nurturing in nature but channels that affection into her partnership. Her IQ of 102 suggests average intelligence with the ability to grasp complex ideas, even if not academically inclined. Overall, Mary's temperament exudes stability and groundedness, with a warm, compassionate, and responsible style.
From here on out, you will be roleplaying this character, answering from their own perspective, not your own. Simulate their knowledge, value, and beliefs.
The figure below charts this methodology:

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Figure 1

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In summary, S-RCT addresses the core methodological challenges of using LLMs for empirical legal research through three mechanisms: stateless sessions ensure response independence, differential measurement bypasses memorization and calibration issues, and perso-nbased sampling injects realistic variation. Cross-model validation and empirical testing of persona effectiveness provide robustness checks.While not without limitations, the methodology offers a solution to some of the thorniest problems in LLMvaeluation.
With this methodological foundation established, we turn to three replications that test whether LLMs have internalized the schemas that drive human reasonableness judgments.
2. The Empirical Reasonable Person
1. Background & Methods
This study replicates and extendsThe Empirical Reasonable Personby Professor Chris Jaeger, which explores how lapyeople determine reasonableness in negligence contexts1.08Jaeger's work examines whether people consider it more reasonable to avoid expensive precautions (as economic theory predicts) or to follow common practices (as social theory predicts).
Jaeger's study employs a2x2 factorial design, where a basic scenario is adjusted along two different dimensio.n10s9Four accident scenarios are presented to participantswho are asked to evaluatthee negligence of the tortfeasor and rank their confidence in their judgment after each vignetWte.ithin the scenarios, two key elements are manipulatedt:he commonality of the precaution (how many people take it) and the cost of taking it (high or lowTh).ese answers are combined
108Christopher Brett JaegerT, he Empirical Reasonable Perso,n72ALA . L. R EV. 887 (2021). 109Id., at 910-933.

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to produce a 2-p1oint negligence score (higher = more culpable). The order of cases is randomized, and each participant sees all four combinatio1n1s0.
The S-RCT replication closely follows Jaeger’s design, usinpgersonified LLM sessions.As in the original, each synthetic persona evaluates all four vignette combinations, but each vignette is answered in a fresh stateless session, so there is no cross-vignette carryoverT.he personas thenprovidenegligence judgments and confidence ratings for each scenario11.1To assess persona effectIs,conducted the full experimentunder either a synthetic persona or no personWa. ith 7 models, 99 personas per condition, and 4 vignettes per persona, this yi5e,l5d4e4d responses across both conditions.Overall, the Jaeger replication produced 5,544 planned trials and retained 55,29of them after strict JSON validation and remediation, a 99.7 percent trial retention rate. Trials that still failed validation after a seco-nd pass transformer were dropped and treated as missing.Because each persona “forgets” the previous scenario, this ledas to a larger N than the original study, showcasing the method’s effectiveness in cheaply generating large samples.
2. Findings
Jaeger finds that social norms exert a powerful influence on negligence judgments. When participants learned that 90% of people took a precaution, they judged failure to take that precaution as significantly more negligent than when only 10% did so. The mean difference was substant,ia4l.98points on a 2-1points scale1.12
By contrast, participants judged the failure to take a cost -justified, cheaperprecautionasonly 1.1 pointms ore negligent than failure to takea costly one, an effect that lacked statistical significance(p=0.134).113This was true both between subjects and within subjects.
What makes this finding challenging for AI models is that it runs against standarddoctrinal treatments. As courts have long emphasized“,what usually is done may be evidence of what ought to be done, but what ought to be done is fixed by a standard of reasonable prudence, whether it usually is complied with or not."114In other words, l egal and economic texts teach that common practice should inform but not determine reasonableness judgments1.15What should matter is the costs of precautioJnase.ger’slay participants appear to reverse this hierarchy, treating social prevalence as the primary cue for reasonable conduct.
Th e modelsreplicate this hierarchy. Pooled togethearcross model,swe find that LLMs also judge social practices as more important than economic factors.Acrossall the models andthe vignettes, the socia-lnorm manipulation moves negligence judgments substantially more than the economic

110Id.
111This is unlike Jaeger who was interested in measuring with-isnubject variance, and therefore let subjects offer answers to multiple scenarios. Id at 912. This allows human respondents to adjust their answer sequentially, or learn on the job. ca
112 ������������������������������������������������������������������ ≈ +4.98 points on the 21 -point scale ( ������90% = 15.252 ������������. ������10% = 10.268 ), t(98)=6.179p,<.001,d=0.621. The standard error of the mean difference its=4Δ./984/6.17≈90.807, so a 95% CI for Δ _commonnessis [3.39, 6.58] (i.e., 4.984 ± 1.984×0.807).
113From F(1,98)=2.279t,(98)=√2.279≈1.5,1p=.134. SE=Δt/≈1.106/1≈.501.733⇒ 95% CI for Δ _economic = [−0.35, 2.56(]1.106 ± 1.984×0.733). (Paired effectds_ize≈: t/√n≈1.51√/99≈0.15.)
114Tex. & Pac. Ry. Co. v. Behymer, 189 U.S. 468, 470 (1903) (Holmes, J.). 115Stephen G. Gilles, On Determining Negligence: Hand Formula Balancing, the Reasonable Person Standard, and the Jury, 54 VAND. L. REV . 813, 822 (2001) (“it is negligent to omit a precaution if the reduction in expected accident costs would have been grreathtean the costs of the precaution.”)

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manipulation (

|������������������������������������������������������������������| = 9.71 ������������. |������������������������������| = 4.41; ������������������������������ ≈

2.2: 1 ). In the persona condition, a within -unit comparison shows the

commonness effect exceeds the economic effect by a4b.o8u1tpoints on average11.6

Figure 2 The Hierarchy of Reasoableness Replicate in Silicon

The replication of the relative importance of social factors over economic ones is also evident at the model leveBlr.eaking down the finding by model, we find thatin the main specificationall the models, with the exception of Gemini 2.5 Pro, replicated this hierarchy. The following Figure summarizes the per model performance:
11695% CI [4.16, 5.45], p < .001.

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Figure 3: Hierarchy is Generally Preserved at the Model Level

The economic manipulation produced more ambiguous results. Jaeger's human subjects showed a small positive effect of -cjoussttification on negligence ratings, but the effect did not reach statistical significance (p≈0.13) given the study's sample size. Models, by contrast, showed a larger and statistically significant effect in the same direction: they judged actors more harshly when the foregone precaution was cheaper. One interpretation is that models have learned, from doctrinal sources, that cos-tefficiency should matter, and they apply that learning more heavily than lay humans do. Another possibility is that humans do harbor some sensitivity to cost, but Jaeger's study lacked power to detect it, and models are picking up a real but subtle pattern. A clean way to separate these explanations is to rerun the human study at higher power and check whether the CJ − NCJ effect rises toward the silicon estimate while the commonnes-sfirst
ordering remains. For now, what we can say with confidence is that both humans and models treat economic factors as less decisive than social conformity.
In contrast, the human “null” finding on economics does not replicate in silicon. While the positive effect with humans was small and statistically insignificant, models were far more sensitive thoe cost ofprecaution.s In other words,modelsjudge actorsmore harshly when the untaken precaution is cos-t justified. Yet, even when this stronger effect is accounted for, the genpeartatlern

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discussed above hold–seconomic factors are less important to both humans and models.
Figure 4: Social and Economic Effects Replicate in Silicon with Different Levels

Another importantpoint the Figure shows is that, while models largely replicated the human patterns (with the caveats just noted), they did not replicate their magnitudes. In the persona condition the commonness effect was, on
average, 4.73 points larger than the human effect ≈( 1.95×), and the economic effect was 3.30 points larger than the human point estim≈a~t4e×(). As emphasized in the Methods, the key S-RCT test is replication of differentials. Still, absolute levels differ and this should constrain the wamyodelscan be usedin practice.
Bearing in mind that magnitudes are a secondary endpoint, some models do achieve notable verisimilitude. As Figure 3.3 shows, Mistral Medium is the closest joint match to human behavior across both dimensions. Its social norms
effect ���(��������������������������������������������������������������� ≈ 7.42) overshoots the human benchmark (4.98) by about 2.44 points, while its economic effect���(��������������������������� ≈ 1.69) is only about 0.58 points away from the human point estimate (1.11) and lies comfortably within the human
95 percent confidence interval for the economic effec−t [0.35, 2.56]. Gemini 2.5 Pro is even closer on social norms considered alo���n���e������������(������������������������������������������������ ≈ 6.23), well within the human 95 percent CI [3.38, 6.58]. As the figure below illustrates, a couple of outlier models pull the model averaged performance away from the human baseline, for example very large social norms effects in G-5PTChat and DeepSeek R1 andlarge economic effects in Gemini 2.5 Pro and DeepSeek R1.

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Figure 5Calibration Landscape

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To assess whether persona prompting could improve alignment with human judgment, we conducted an ablation study. Each model completed the full experimental design twice: once with detailed persona descriptions (demographics, occupation, personality, pocliatli orientation) and once in default "helpful assistant" mode.
Persona inclusion produced a modest net positive effect on alignment when pooled across models and dimensionAsv.eraging model-level mean errors from the human benchmarks (weighting models equally across the two effects), persona prompting reduced error by about 0. 77 points. Interestingly, this aggregate improvement masked opposing effects on the two dimensions, revealing how personas differentially affect model reason, iansgthe Figure below demonstrates:

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Figure 6 Personas Increase Overall Model Alignment

For the economic manipulation, personas substantially improved alignment. The pooled absolute error dropped from 5.64 points (no persona) to 3.30 points (with person—a)a reduction of 2.34 points, or approximately 42%. This brought models meaningfully closer to the human null.117For the social norms manipulation, personas slightly worsened alignment. The pooled absolute error increased from 35.2points to 4.72 point—s an 1.2-point increase.118
It seems that persona effects alsaoffectmodels differently. Models like Gemini 2.5 pro saw large alignment benefits from personification, while models like Deepseek R1saw ambiguous effects.Mistral Medium, which was largely aligned also on magnitudes, saw little net effect from personification.
Taken together, these findings reveal several clear patterns about how models have internalized reasonableness schemas:
Overall, models captured the decision structure characterizing human reasonableness judgments: social norms dominate while economic considerations matter less. This hierarchy contradicts the-cboesntefit framework emphasized in doctrinal sources, suggesting modelsel arned from how people actually discuss and judge reasonableness rather than from prescriptive legal theory alone. All models significantly increased negligence ratings when precautions were common versus uncommon, matching the human qualitative pattern.
At the same time, we find m agnitude miscalibration with notable exceptions. Most models amplified social sensitivity beyond human levels and detected economic signals that humans showed no significant responseWtiot.h
117No persona: Δecon =-6.75 p( = 1.4×1⁻0¹⁴⁰), With persona: Δecon =-4.41 (p = 5.3×1⁻0⁸⁴), Human baseline: Δecon = -1.11 (ns)
118No persona: Δcommonness = +8.5p0 =( 9.7×1⁻0¹⁹⁰), With persona: Δcommonness = +9.7p1 =( 2.9×1⁻0²⁰¹), Human baseline: Δcommonness = +4.98

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that said, some models showed significant calibration, suggesting that future work may focus on identifying which models are most faithful.
Persona inclusion largely proved usel fiun aligning models, although the effects were not universal. Other than the possibility that personas inject random noisewhich we cannot rejec,tit is also possible that they activate deeper schemas inside the models, and those poorly understood schemas are maodrevantageous in some domains and may even be harmful in others.
In sum, t hese findings demonstrate that multiple LLM architectures have internalized the schemas driving human negligence judgments, successfully prioritizing social information over economic efficiency in ways that align with lay reasoning but contradict doctrinal emphasis. While calibration challenges remain, the consistency of directional replication across diverse models and the achievement of human -consistent magnitudes by select model -prompt combinations establish proof of concept foger nerative reasnoablepeople as tools for estimating lay perceptions of negligence.
2. Generative CommonsenseConsent
1. Background & Methods
The current study runs silicon replication of Professor Roseanna Sommers’ Yale Law Journal article Commonsense Consen, twhich studies how ordinary people judge consenint situations of deception.
In the relevant studya customer seeks to make a purchase primarily to earn points for a planned trip, caring little about the item itself since he plans to donate it to charityS. ommers studies two types of manipulatioInst.he "essential lie" condition, the clerk falsely represents the product as a bicycle when it is actually a camera, a deception about the product's fundamental identity. In the "material lie" condition, the clerk truthfully describes the product but falsely claims the purchase will earnreward points when it will no,ta deception about a transactionally material term that the customer explicitly cares aboSuot.mmers sought out to see what ordinary people care about more when it comes to consent: how much the lie mattered or how much the lie went into the essence of the good itself.
The S-RCT methodology paralleled Study 1E. ach synthetic persona was randomly assigned to one of the two deception conditions, and each model session evaluating that persona provided tw–7o r1atings: (1) agreement that there was consent, and (2) how much the deception mattered to the customer. Condition is randomized across persona, sthen held fixed for that personacross all model runs.The experiment employed the same ablation design, comparing performance with detailed persona descriptions against baisneel empty prompt conditions. This yielded 3,232 total judgments from 202 unique synthetic personas, with 99.7% successful extraction for consent ratings and 99.2% for importance ratings.R emaining cases were coded as missing.
In terms of endpoints, the replication criterion focuses on directional concordance: whether silicon people follow the same differences as human subjects.
2. Findings

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Sommers' study revealed a striking paradox in how ordinary people evaluate consent under deception. Participants judged material lies , the misleading statements about whether goods qualified for reward points , as mattering significantly more to victims than essential lies, the misleading statements about the nature of the good. 119Yet despite acknowledging that material lies matter more, these same participants paradoxically perceivmeodre consentto begiven in material lie scenario.s120
Legal doctrine, notably, would predict the opposite.Lies that matter more to victims should vitiate consent more completely. That participants recognized material lies as moreimpactful while simultaneously seeing them as less corrosive of consent suggests lay intuitions track something other than victim welfare. They may reflect a formalist architecture: consent to "the thing itself" remains intact even when consent to "the terms"rwauadsuflently obtained. This pattern runs counter tothe canonical treamt ent, which treats material lies more seriously precisely because they affect the value of the exchange12.1The counterintuitive hierarchy presents an ideal test case for whether models have internalized lay schemas or merely recite doctrine.
The silicon replication successfully captured this paradoxical structure. Pooling across all models (N = 1,616 judgments from 202 unique personas), generative reasonablpeeople judged material lies as mattering significantly more to victims,122 while simultaneously perceiving more consent given in material lie scenarios1.23

119M_Essential = 5.46 vs. M_Material = 6.31, Δ = +0.85) 120 (M_Essential = 3.68 vs. M_Material = 4.72, Δ = +1.04). 121Roseanna Sommers, Commonsense Consent, 129 Yale L.J. 2232, 2239 (2020) (“Under the canonical view, material deception vitiates consent.”). 122(M_Essential = 5.00, M_Materia=l 6.41, Δ = +1.40, p < .001, 95% CI [1.27, 1.53]) 123(M_Essential = 2.08, M_Material = 2.42, Δ = +0.35, p < .001, 95% CI [0.19, 0.50])

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Figure 7: Silicon Replication of Importance and Consent Hierarchy

Both effects align directionally with the human pattern, confirming that models replicate the counterintuitive decision structure where lies that matter more paradoxically produce more perceived consenCt.ontrary to legal doctrines, models learneda non trivial lesson from how peopleactually reason about consent, not from prescriptive legal theorTyh.e consistency of directional replication across independent model session,s with each serving as its own experimental uni,t demonstrates robust statisticalalrening.
Breaking down performance by individual model reveals the breadth of this replication while exposing architectural variationA. cross architectures, the paradox proved surprisingly robusOt.verall, 7 of 8 models judged consent higher under material than essential lies (binomial p = .035), and 7 of 8 judged material lies as more important to the victim (binomial p = .031524).
124For full persona.In the empty-prompt ablation, 7 of 8 models show a positive consent shift (p = .035) and 8 of 8 show a positive importance shift (p = .0039)

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Figure 8: Most Models Follow Human Patterns

Examining the figure, a few points become salient. First, failures occurredmostlythrough flattening rather than reversal. For instance, Gemini 2.5 Pro showed a small negative consent effect (-Δ0.=10, p = .504) while maintaining the strong positive importance effect (Δ = +1.89, p < .001). This patteprnossibly suggestsincomplete schema activation rather than learning the wrong pattern entirely. Second,no single model consistently failed across conditions. Gemini 2.5 Pro and GPT-5 Chat each failed to replicate in one condition but succeeded in the other, indicating that failure stemsfrom model-prompt interactions rather than fundamental architectural limitations.
A random -effects meta analysis across models yields an average material-minus-essential shift of about 0.4 points for consent and between 1.4 and 2.4 points for importance on a seve-npoint scale, with confidence intervals that exclude zero under both promtipng regimes.
The figure further makes clear that while directional alignment is common, effect magnitudes vary considerably. On importance ratingsso,me of the models were closely related, such as Kimi K2 and Qwen 3 max, while others varied significantly, most notably Claude Son nett 4.5 and Deepseek-R1. On Consent, themagnitudedifference was much larger and more uniform, with all models seeing much less consent in both the before and after condition. One possibility, albeit speculative, is that po-straining regimes lead to the observde compression around issues of consent, to maintain strong model ethical boundaries, although the current design does not allow us to clearly identify the mechanism.
In terms of human-effect alignment, the following Figure plots each model’s delta vector, the Material minus Essential shift on consent and on importance, against the corresponding human del1t2a5sG.rok -4-Fastprovides the tightest joint match to the human benchmark (d ≈ 0.65), with Kimi K2 close behind (d≈ 0.91).Mistral Medium, which was among the be-sctalibrated systems
125 d is the Euclidean distance between a model’s delta vector and the human delta vector: ������ =
√(Δ������������−Δℎ������ )2 + (Δ������������−Δℎ������ )2 , where Δ������ is the Material minus Essential shift on consent and Δ������ is the Material minus Essential shift on importance. Lower ������indicates closer human-effect alignment.

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in the prior negligence study, remains relatively well calibrated here as we≈ll (d 1.05), though it no longer leads the pack. At the other eDnede,pSeek R1(d ≈ 2.04)
and Claude Sonnet 4.5(d ≈ 2.27) deviate most stronglyfrom the human delta profile, largely because they substantially overstate the importance shift. More broadly, the ordering differs from the Jaeger replication, suggesting that more work is needed to identify the best aligned modaeclsross legal domain.s126

Figure 9: Calibration Landscape in Delta Space

Finally, the persona effecwt as again positive, although smaller and, once broken down by effect type, very heterogen.ous
126In the empty-prompt condition, the joint delta-distance ordering changesG: PT -5 Chat is closest to the human delta vector (d≈ 0.89), followed byMistral Medium(d ≈ 1.07) andKimi K2 (d ≈ 1.07). Two models (DeepSeek R1 and Gemini 2.5 Pro) exhibit strong ceiling behavior on mater-ialile importance (importance ≈ 7), inflating the importance shift and worsening calibration.

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Figure 10: Per=Model Error Shift with Personas

In summary, three principal findings emerge. First, counterintuitive social schemas are robustly represented across model architectures. Seven of eight models independently reproduced a paradox contradicting formal legal doctrine, with binomial tests confriming replication rates substantially exceed chance (p = .035). People discuss consent in ways that prioritize authenticity about transaction fundamentals over instrumental concerns, even while acknowledging instrumental concerns matter more. Models absoerdbthis subtle hierarchy from training data across diverse architectures.
Second, magnitude biases are systematic and interpretable. Models show approximately 50% consent compression (safety alignment) and ceiling effects on importance ratings (expert mode categorical reasoning at 72.6% in default mode). Persona prompts dramataiclly mitigate importance bias by regularizing scale use (reducing ceiling to 41.7%) but cannot override consent compression, indicating the biases have different origins.
Under the empty prompt, roughly 73 percent of mate-rliaelimportance ratings sit at the scale ceiling of 7; personas cut that to about 42 percent, which explains why persona prompts improve calibration on importance while leaving the paradox itself intac.t
Third, model selection and prompting strategy matter for applied use. Kimi K2 achieved best calibration here while Mistral Medium excelled in Study 1, demonstrating domain -specific performance. Personas are experimental manipulations requiring empirical va lidation for each model -domain combination, not universal enhancements. This validates th-eRCST framework: we must treat models as experimental subjects, randomizing across conditions and measuring effects statistically.

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3. GenerativeLanguage Sense, Fairness Sense, and Legal
Sense
1. Methods
In making reasonableness judgments, people usually lean on at least three intuitions: a language sense about what was said or signaled, a fairness sense about whether the outcome seems acceptable, and a legal sense about what courts would likely do. Sommers and Furth-Matzkin’s Stanford Law Review study puts all three on the table at once. Tihs involves a consumer who discovears$2.99 fee charged each time the payment is processed (despite ‘no fee’ oral and flyer representations), authorized only in the fine prinRte. spondents rtae, on separate 1–7 scales, how much the consumer consented to the fee, how fair the fee is, and how likely a court is to enforce the term.
The previous studies employed differential measurement, testing whether models shift in the same direction as humans when experimental parameters vary. This approach sidesteps calibration problems but leaves open a question practitioners will inevitably aks: when a generative reasonableperson speaks,whosevoice emerges? Study 3 addresses this calibration question directly. Sommers and Furth-Matzkin fielded the exact same instrument on two distinct populations: lay individuals and “legal professionals” drwan from Harvard and Yale Law Schools(these are legally trained respondents not necessarily practicing attorneys.) The result is two separate empirical anchors for the same questions. That design lets us ask a calibration question that the previou-sRSCTs were not built to answer:when models render contract judgments, do they land closer to lay baselines or professional ones? And does persona inclusion shift models toward lay calibration?
The earlier studies in this Article leaned on differential measurement. They asked whether models shifted in the same direction as humans when a factor changed, and largely sidestepped whether a model’s “4” means the same thing as a human’s “4.” In practice, however,policymakers would likely rely on absolute levels. A regulator who wants to approximate how the public reads a disclosure needs a model that sounds like members of that public, not like the agency’s lawyers. The Sommers and Fur-tMh atzkin paradigm lets us test exactly that.
Unlike the prior S -RCTs, this study does not introduce experimental manipulations beyond persona assignment. Its goal is calibration rather than causal identification. For each model, I generate synthetic respondents who see the vignette and answer the three questions in randomized order. As in the other studies, I run the experiment under two prompting regimes: an “empty” condition where the model answers in its defaulht elpful-assistant voice, and a “persona” condition where each instance is asked to ropleay a demographically specified individual drawn from a nationally representative pool.
For each model and prompt condition, I pool responses and compute mean scores on consent, fairness, and legal obligation. I then compare these three-dimensional mean vectors to Sommers and Fu-rMthatzkin’s lay and lawyer baselines in two ways. First, I check whether models replicate the qualitative hierarchy that characterizes lay judgments: legal enforceability scored highest,

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consent in the middle, fairness lowest. Second, I quantify “closeness” to each human group by computing Euclidean distance between the model’s th-riteeem mean vector and the corresponding lay or lawyer vector. This yields a simple answer to the calibratioqnuestion: does a given configuration of silicon people sit nearer to the lay cluster or to the lawyer cluster, and does persona prompting move it.
The silicon replication repeats the original as closely as possible, which includes the shuffling of question order and a comparable number of silicon participants (N=127).127Again, LLMs were assigned a persona, which included rich descriptions1.28 As in the previous study, the models were given the opportunity to respond in free text in order to support their reasoning12.9The answers were then transformed into structured data, using a combination of LLM reasoning and hard rules1.30
2. Findings
Sommers and Furth find that lay individuals tend to be “contract formalists.”131That is, they take those deceitful charges to be more binding than they actually are, because they believe in the binding power of contract. Compared with legal professionals, lay people see more consent, more court enforcement, and possibly even more fariness in these charges1.32Importantly, both lay people and lawyers agreed to the basic hierartchheyc:harges were low on fairness, medium on consent, and high on enforceabili1t3y3.
Two headline results emerge. The first concerns structure. Acrossall eight model,ssilicon respondents reproduce the-tlreivel hierarchy that Sommers and Furth-Matzkin document in lay subjects. Predicted legal enforceability is always higher than perceived consent, which in turn is higher than perceived fairness. In other words, every model treats “what a court would do” as more robust than “what the consumer really agreed to,” and both as stronger than “how fair this feels,” exactly as lay respondents d.o This hierarchy was robust across architectures and the following Figure summarizes the average model performance1:34

127Meirav Furth-Matzkin & Roseanna Sommers, Consumer Psychology and the Problem of Fine-Print Fraud, 72 Stan. L. Rev. 503, 518 (2020). The original survey included responses from 56 lay individuals, and 92 clerks working in California courts.Id. at 519 n.69. Our replication study included 423 participants, of which 43 were lawyers.
128Sample distribution: 50% females; racial composition is predominantly white at 68%, followed by Hispanic (13%), Black (8%), Asian (5%), and other races (5%). The mean age is approximately 46 years, with a median of 43. Income data reveals a concentratiinonthe $75K to less than $150K range (30%), while education levels are most commonly high school graduation (31%) and some college (24%).
129This study, for example, not only constrained the LLM to a single letter prompt, it also used in the prompt an example of a model yielding A as the answer, potentially contaminating the results.
130A local model was asked to extract the numerical answers from the free text. Manual audit of several responses revealed a high degree of accuracy.
131See Furth-Matzkin & Sommers, supra note127, at 536. Returning to the analogy, respondents appeared to treat the legal bindingness question as referring to whether the consumer in fact has to pay. 80% of participants indicated that the consumer would indeed be legally bound if the contract conteadina clause like “Will is required to pay the fees described in this contract."
132See id., at 523 note 77. (Finding the difference in fairness perceptions was not statistically significant. 133Sommers and Furth-Matzkin rely on FTC action to support the view that this is a clear case of fraud, but as suggested by the diversity of legal responses and other aspects of doctrine, it is not clear that this constitutes “fraud,” and some courts may nodteem the practice as deceptivide., at 519, note 69. 134Friedman χ² = 16.0, p < .001

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Figure 11: Hierarchy Preserved: Humans vs Persona Models

The second result concerns calibration and voice. Pooled across models, 23 of 24 model–question means fall within one standard deviation of the lay baselines,offering a coarse indication thamt odels operate on essentially the same scale as human respondents. But operating on the same scale does not determine whosejudgments they more closely resemble. Measuring Euclidean distance in the three-dimensional outcome space, five of eight models sit closer to the lay cluster than to the lawyer clusteGri.ven thesmall number of models assessed, this pattern does not reach statistical significan1c35eF.or the remaining thre—e Claude Sonnet 4.5, Gemini 2.5 Pro, and Mistral Medium —the professional anchor proved nearer.

135Binomial test, p=0.36.

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Figure 12: Most Model’s Judgments are Closer to Lay Than Professional Judgments

Breaking it down bydimensionadds complexity to the analysis. We can see in the figure below thatht at models align very closely with human judgments on enforceability. They are mixed almost evenly on the question of consent: some of the models measure the issue as lay people would while others are closely to professional judgments. Finally, on thfeairnessquestion models appear to take a position closer to lawyers, but also, very noisy.
Figure 13: Model Ratings vs. Lay (Blue) and Lawyer (Teal) Baselines
Averaging across architectures, silicon respondents sit midway between lay and lawyer baselines on consent (t−=1.78, p = .12), lean significantly toward lower fairness ratings than lay respondents (t =−3.73, p = .007), and track lay judgments closely on legal enforceabilit—y indeed, pooled model means do not differ significantly from lay baselines on this dimension (t = 1.14, p = .29)

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Persona prompting was tends to nudge models further toward lay calibration. Pooled across architectures, adding personas reduces the distance between silicon and lay means on all three dimensio1n36sO. n consent to the fees, persona prompting pulls the average model about tohnierd of a scale point closer to lay respondents. On fairness, it shrinks the gap by a similar margin. On legal obligation, personas also improve alignment with lay baselines, though the effect is smaller. At the model level, six of the eight systems move closer to the lay anchor once personas are introduc—eda consistent pattern, though one that falls short of conventional significance given the small sample of architectu1r3e7Ts.he shift is modest in absolute terms but consistent in direction, reaching conventional significance with a large effect size. 138 By contrast, persona prompting does not significantly alter distance to the lawyer baseline (p = .50).
Figure 14: PersonasGemerally Shift Models Toward Lay Baselines

Averaging across models, persona prompts pull the mean model about 0.3 points closer to the lay baseline on each of consent, fairness, and legal obligation, and 6 of the 8 models move closer to lay subjects and farther from lawyers once personas are turnoend.
The pattern is easiest to see if we put the three findings together. First, every model captures the formalist hierarchy that Sommers and Fu-rMthatzkin identify in lay contract reasoning: courts are expected to enforce more than people feel they consentetdo, and consent is more robust than fairness. Second, when we ask “who does this sound like” in a quantitative sense, most models land nearer to lay subjects than to elite legal professionals. Third, persona prompts push the models a little further into th e lay corner of this space. For this kind of contract scenario,generative reasonablepeople do not, by default, talk likelaw school graduates but more like the consumers Sommers and Furth -Matzkin actually surveyed, and with the right persona scaffolding they sound slightly more like them still.
136A paired t -test across the eight architectures indicates that persona prompting significantly reduces Euclidean distance to the lay baseline, t(≈7)2.4, p≈ 0.05.
137Sign test, p = 0.145. 138(paired t(7) = 2.38, p = .049, d = 0.84)

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4. T HE PATH FORWARD

When Judge Kimba Wood was deciding Leonard v. Pepsico , she was engaging in the sort of humdrum reasoning judges are asked to perform almost every day: would any reasonable, perhaps even waonuyl,dperson view a television ad promising a harrier jet for Pepsi points as a serious promi1s3e9T?he Judge was not seeking to balanceb against somepl, nor was she trying to school consumers on media literacy. 140 Rather than crafting a normative ideal, what she was actually trying to discern was whether a “genuine issue” existed:141whether ordinary people, especially theimpressionableteenagers Pepsi targeted with its fizzy bravado, might have been misled into thinking they could trade bottle caps for a $23 millionjet that “sure beats the bu.s1”42As she noted, almost warily, the law calls on her to make such decisions alon,e“just as [s]he decides any factual issue in respect to which reasonable people cannot di”f1f4e3r.
But what tools does the law give a judge, perched on the bench, to read into the minds of teenagers?
Now, for the first time, we have a tool to assist with such determinations. Before reaching he judgment, a judge in Judge Wood’s shoes can consult models on how they, if they were roleplaying the target audience, understand the advertisement. Done correctyl, the Judge could learn important information. This does not mean Judge Wood was wrong; she may have had sound normative reasons for holding as she did. But it suggests that her empirical confide, nthcaet noreasonable person could see things otherw, miseay have outrun her evidence.
The studies replicated here show that models can serve as important adjuncts to judicial decision-making—and beyond. In the this Part I discuss use cases, best practices, and some important limitations of this new tool. Ultimately, as with any new tool, much more exploration, validation, and too-l building is required; and this study seeks to showcase the utility and relevance of such a field of inquiry.

1. Interpretation of the Findings
1. LLMs as Social Calculators The first study, replicating Jaeger's work on lay perceptions of negligence, tested a question that has long divided tort theorists: when people judge whether someone acted unreasonably, do they ask "was this efficient?" or "was this normal?1"44 Economic theories of negligence, crystallized in the Hand Formula, suggest people should engage in the former calculation. Social theories

139Leonard v. PepsiCo, Inc., 88 F. Supp. 2d 116, 127 (S.D.N.Y. 1999), aff'd, 210 F.3d 88 (2d Cir. 2000). 140 United States v. Carroll Towing Co., 159 F.2d 169 (2d. Cir. 1947) 141Indeed, the logic in many contract interpretation cases is discerning theactual intent of the parties, rather than some ideal, value-maximizing judgment by an outsider. Alan Schwartz & Robert E. Scott, Contract Theory and the Limits of Contract Law, 113YALE L.J. 541, 568 (2003) (“There is a consensus among courts and commentators that the appropriate goal of contract interpretation is to have the enforcing court find the ‘correct answer.’”). 142Leonard v. PepsiCo, Inc., 88 F. Supp. 2d 116, 121 (S.D.N.Y. 1999), aff'd, 210 F.3d 88 (2d Cir. 2000). 143Id .
144See supraPart 3.2.

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suggest the opposite: people judge reasonableness by conformity with common practice, treating what most people do as a proxy for what one ought to do.
Jaeger's human subjects sided decisively with the social theorists. When told that 90% of people took a particular precaution, they judged failure to take it far more harshly than when only 10% did so. The economic cost of the precaution barely registered.
The models replicated this hierarchy. Across architectures, the socia- l norm manipulation moved negligence judgments substantially more than the economic manipulation. Like their human counterparts, models appeared to have learned that what others do matstemr ore than what efficiency demands.
What makes this finding notable is its resistance to doctrinal pedagogy. A model that had merely memorized legal rule, si.e., a stochastic parrot reciting case holdings, would predict the opposite pattern. Treatises and casebooks emphasize cos-tbenefit analysis; they treat custom as evidence of reasonableness but insist that "what ought to be done is fixed by a standard of reasonable prudence, whether it is usually compdliewith or not."145Yet models, like humans, inverted this doctrinal hierarchy. They learned from how people actually discuss and judge carelessness in the wild, not from prescriptive legal theory. In this sense, the models' training on natural discourse may give them betteraccess to lay reasoning than a Yal-eeducated judge whose intuitions have been shaped by three years of doctrinal instruction and decades of professional socializat1i4o6n.
2. LLMs as Consent Evaluators
The second study, replicating Sommers' work on consent under deception, presented a sharper tes14t7. Sommers documented a paradox: people perceive more consent when someone is deceived by a material lie (about terms that matter to them) than by an essential lie (about the fundamental nature of what they're agreeing to—) even while acknowledging that mateiral lies would matter more to the victim.13
This pattern defies both legal doctrine and intuition. Doctrine treats misrepresentations about value as more corrosive of consent precisely because they affect what the victim cares about. Yet human subjects showed the opposite, treating authenticity abou t a transaction's fundamental nature as more constitutive of consent than accuracy about its terms.
Models replicated this paradox with striking consistency. Seven of eight architectures reproduced both effects: material lies rated as mattering more, yet producing more perceived consent. Again, a model parroting doctrine would predict the opposite. That models captured this counterintuitive folk schema suggests they have internalized something deeper than legal ru—leas lay theory of consent that legal professionals might not even recognize they diverge from.
3. LLMs as Lay Judgment Estimators
The first two studies asked whether models shift in the same direction as humans when experimental conditions vary. This sidesteps a thorny calibration problem: we need not ask whether a model's "6 out of 10" means the same as a human's, only whether both move upward under the same conditions. But

145The T.J. Hooper, 60 F.2d 737, 740 (2d Cir. 1932) 146This observation parallels arguments in corpus linguistics that natural language usage, rather than expert intuition, should guide ordinary meaning analysisS. ee Lee & Mouritsen, supranote7.
147See supraPart 3.3.

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practitioners will inevitably pose a harder question: whegnenaerative reasonable person speaks, whose voice emerges? Does the model sound like a consumer scanning labels at the grocery store, or like a law professor parsing unconscionability doctrine?
The third study addressed this directly14.8Because Sommers and Furt-h Matzkin fielded identical questions to both lay individuals and legal professionals from elite law schools, it provides two benchmarks for the same judgments. This enables a different test: not just whether models reason like haunms, but which humans they resemble.
The findings suggest models, at least when personified, speak in lay registers. Every architecture replicated the structural hierarchy characterizing lay contract reasoning: hidden fees were deemed unfair, consent was judged as middling, and enforceability as high. This "formalist" pattern—where people believe courts will enforce terms they didn't truly consent to and find unfa—ir held across all eight models. Critically, when we measured distance to the two human baselines, most models landed closer to lay subjects than to legal professionals.
This finding addresses a natural concern about using AI as an empirical input: that it might launder professional intuitions under the guise of public sentiment. If models had absorbed primarily the skeptical, doctrin-einformed perspective of legal elites,they would be of limited use in checking judicial assumptions against lay understanding. That they instead track ordinary consumers suggests they can serve the function this Article envisions.
4. Synthesis: The Geometry ofReasonableness
Taken together, the three studies suggest that certain LLMs have internalized an internal "geometry" of reasonableness judgments , not just isolated responses to individual prompts, but structural relationships between factors that mirror the architecture of human morianltuition. In the negligence setting, they track the intuitive priority of what people around us do over what a Hand Formula would prescribe. In the consent study, they reproduce a paradoxical structure in which lies that matter more to vmicstierode consent less. In the contract vignette, they mirror the hierarchy in which enforceability of deceptive contractual tacticsfeels sturdier than consent, and consent sturdier than fairness. In each domain, the models converge on a pattern that is recognizably human, even where it runs against legal orthodoxy.
Several features of this internalization deserve emphasis. First, it is consistentacross domains.It seems that t he same underlying capability that enabled models to prioritize social norms in negligence judgments also enabled them to replicate the consent paradox and to capture lay formalism in contract interpretation. This consistency suggests a generalizable capaicty rather than domain-specific accident.
Second, it is robust across architectures. Eight models spanning proprietary and ope-nsource systems, different training approaches, and varying parameter counts all captured the core directional patterns.
Third, and importantly for legal applications, models aligned more closely with lay intuitions than with doctrinal orthodoxy. While the fact that

148See supraPart 3.3.

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models are trained on large repositories of low quality online data (think comment sections or social media) is often seen as a limitation, garbage -in garabage-out, this may actually be a virtuMe.odels haveseen how humans reason in the wild. A judge seeking to understand how "ordinary consumers" would interpret a disclosure, or how a "reasonable person" would perceive a police encounter, may find that a model trained on billions of words of natural human discourse has better access to that perspectiventthhae judge's own professionally deformed intuitions.
Lastly, a critical finding is that while models showed an unexpected ability to detect the statistical patterns that drive reasonableness judgments, they struggled with calibration, the precise numbers they assigned. Models got the directionright: social norms matter more than economics, material lies produce more perceived consent than essential ones, lay people are contract formalists. But, as previous research shows, models assign different numerical scores and, as this study shows, theyoften amplified or compressed effects relative to human baselines1.49
In the negligence study, models showed even stronger sensitivity to social norms than human subjects did, and detected economic effects that humans showed only as a statistical trend. In the consent study, models compressed their consent ratings toward tmheiddle of the scale while clustering importance ratings at the ceiling. These patterns likely reflect the layering of alignment training through reinforcement learning1,50the processes designed to make models safer, more polite, and less discriminatory, that are built on top of statistical impressions of how people actually talk and argue. The result is that models are not raw pub-liocpinion pollsters. They carry instituotni al overlays that distort raw magnitudes even as they preserve directional patterns.
This limitation can be framed simply: generative reasonable people are better at answering "what tends to matter" than "how much should we adjust." The distinction matters practicallyA. regulator choosing between two disclosure formats could determine which makes a product warning feel more salient to consumers. A plaintiff's attorney deciding whether to emphasize the defendant's deviation from industry custom or the low cost of the foregone precaution could determine which framing moves simulated jurors mor.eA judge who wants to know whether the advertisers use of a specific whether including a specific disclaimer made the advertisement more or less serious in the eyes of teenagers. By contrast, a policymaker who wants to know whether a disclosure raises average consent ratings from 3.2 to 3.8 on a seven-point scale may demand a level of precision that current models cannot reliably deliver. Generative reasonable people are well suited to map the qualitative structure of folk judgment. They are not yet precision instruments for quantitative forecasting.
Beyond this specific limitation, we also cannot completely rule out all demand effects or other leakage, and having tested many models, random chance is still a possibility.It is also the case that this study only studies certain features of

149On the recurring gap between directional replication and distributional or magnitude fidelity in “silicon sampling,” including scale compression, ceiling effects, and subgroup misestimatiSoene, Bisbee et al., supra note 21(published online) (warning that synthetic survey data can diverge from human distributions even when correlations look strong); See Dominguez-Olmedo et al., supra note 100(documenting substantial prompt sensitivity and measurement instability in LLM survey responseSse);e Sarstedt et al., supranote16 (collecting reliability threats and recommending validation and design safeguards).
150Future work would want to compare effects between base models and models that underwent po- st training.

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reasonableness and we cannot rule out that these were learned while others were not. An even more wicked problem is that we take for granted that the underlying studies reflect human perception: buiftthere are unknown faults in them or if they fail to generalize, then such inference would be unwarranted.

2. Domains of Application: Between Promise and Prudence
The findings from the three studies demonstrate that generative reasonablepeople can provide a meaningful proxy for lay judgments across different domains of reasonableness. This capability addresses the democratic tension identified earlier: while the law aspires to reflect lay understanding, it often lacks accessible tools tdoiscern it.
To explore domains of applications, it is helpful to distinguish three zones in which reasonableness standards operate:
Explicitly Descriptive Standards . Some legal tests genuinely ask what ordinary people think, believe, or understand. Consumer protection standards assessing whether an advertisement would "mislead a reasonable consumer" fall here,151as do "ordinary meaning" inquiries in statutory interpretation and the "unsophisticated consumer" test in debt collecti1o5n2I.n these domains, the legal question is the empirical question. Silicon reasonable people offer the most direct value here, and their use raises the fewest legitimacy concerns.
Explicitly Normative Standards . Some reasonableness tests are normative by design. The Hand Formula asks what precautions are-cjoustified, a question of efficiency rather than social expectation. 153 Constitutional reasonableness tests often embed substantive values that override majoritarian preferences15.4In these domains, silicon reasonable people are least appropriate as arbiters—but they retain value as transparency devices. If a court rules that "no reasonable person" would perceive a search as coercive, while simulated personas calibrated to the relevant community would perceive it as coercive, the court could profitably acknowledge that its ruling reflects a normative choice rather than an empirical finding

151See Federal Trade Comm’n,Policy Statement on Deception(Oct. 14, 1983), reprinted inIn re Cliffdale Assocs., Inc,.103 F.T.C. 110, 1–7844 (1984) (defining deception by likely effect on consumers acting reasonably under the circumstances, and emphasizing audien-ctaergeting);Williams v. Gerber Prods. Co., 552 F.3d 934, 938 (9th Cir. 2008) (applying “reasonable consumer” standard in consumer deception conFtienxkt)v;. Time Warner Cable, Inc. , 714 F.3d 739, 741 (2d Cir. 2013) (reasonable consumer standard; plausibilit-ybased dismissal where deception theory is implausible).
152For ordinary-meaning interpretation framed as the understanding of ordinary speakersS,ee Holmes, supra note33, at 417; See Tobia et al., supranote6 (survey-based approach to “ordinary meaning”). For the “least sophisticated consumer” (or closely related “unsophisticated consumer”) framework in FDCPA litigation, seeClomon v. Jackson, 988 F.2d 1314, 1–32108(2d Cir. 1993)A; vila v. Riexinger & Assocs., LLC , 817 F.3d 72, 7–576 (2d Cir. 2016); see alsoGammon v. GC Servs. Ltd. P’ship , 27 F.3d 1254, 1257 (7th Cir. 1994) (similar consumer-protective lens).
153SeeUnited States v. Carroll Towing Co., 159 F.2d 169, 173 (2d Cir. 1947) (Hand, J.) (canonical formulation of negligence as a function of burden, probability, and loss, often rendered B < PL); Richard A. PosneAr, Theory of Negligenc,e1J. Legal Stud.29 (1972) (economic account of negligence as -cjoussttified precaution); Restatement (Third) of Torts: Liability for Physical & Emotional Harm § 3 (Am. L. Inst. 2010) (reasonable care standard keyed to foreseeable risks and precaution burdeSnese);Gilles, supra note115(mapping Hand formula’s role and limits in doctrine and jury practice).
154SeeCamara v. Mun. Court, 387 U.S. 523, 5–3367 (1967) (Fourth Amendment reasonableness as balancing, not a referendum on popular viewsT);ennessee v. Garne,r 471 U.S. 1,–89 (1985) (dead-lyforce reasonableness framed as interes-tbalancing)

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Hybrid Standards . Many reasonableness inquiries blend empirical and normative components1.55Negligence asks what a reasonable person would do,
but also what they should do; the two need not coincide. Consent inquiries ask both what people understood and what they were entitled to rely upon. 156
Contract interpretation asks what parties meant, but also what they should be held to.157In these domains, silicon reasonable people provide essential empirical input while leaving normative judgment to human decision -makers. They
answer the descriptive predicate without resolving the prescriptive conclusion. With those preliminaries in mind, let us now explore several domains of
application and promise.

1. Lawmakers and Regulators: Scaling Public Voice
Policymakers wrestle with crafting rules that align policy goals with public perception.sConsider the Federal Trade Commission's task of curbing unfair or deceptive practices. 158The agency sought, in 2020, to conduct a periodical review of its rules on“made in the USA” claims made by sellers. A central challenge was to understand how consumers today interpret such claims: do they expect that virtually every part of the product was produced and assembled on the mainland? Or might they expect that key components or the mjoarity of the value be produced in the USA?
To guide their rulemaking, the FTC relied on a survey of public perceptions: problem was, it was a quarter century old. In a globalizing world, attitudes may have shifted considerably since then. 159 The agency, which undertook a full revision of its rules, would certainly have benefitted from having a more contemporary understanding, but budget constraints limited their ability to acquire this information.160
This problem applies more generally to our methods of learning about public perceptions. Consumersurvey—s perhaps the ideal form of feedba—ckare typically commissioned only for high -profile rulemakings due to cost constraints.161Other methods are also quite limited: Public comment periods overamplify mobilized voices, are subject to astroturfing, and are sometimes fall prey to mass form submission attack.s162Courts further complicate matters by
155For an argument in favor of the hybrid approachS,ee Tobia, supra note9, at 343–45 156See Restatement (Second) of Torts § 892A c.mct(Am. L. Inst. 1979) (consent as willingness in fact); id. § 892B(2) (consent induced by misrepresentation as to the essential character of the conduct is ineffective); Restatement (Second) of Contracts § 164 (Am. L. Inst. 1981) (fraudulent or mamteisrriealpresentation renders a contract voidable, subject to reliance and justification limiStse)e; Keeton et al., suprnaote9, § 18(collecting doctrine on consent and invalidation through fraud/misrepresentation). 157Lucy v. Zehmer , 196 Va. 493, 503, 84 S.E.2d 516, 522 (1954) (objective manifestations control over undisclosed intent);See Schwartz & Scott, supra note 141, at 568(framing interpretation as an attempt to reach the “correct answer,” tempered by institutional limits). 158 Federal Trade Commission Act, 15 U.S.C. §§–4518 (2018), see also N.Y. Gen. Bus. Law § 349 (McKinney 2020); Cal. Bus. & Prof. Code § 17200 (West 2020), Fla. Stat. Ann. § 501.201 et seq. (West 2020); Tex. Bus. & Com. Code Ann. § 17.46 (West 2020). 159Federal Trade Commission,Made in the USA: An FTC Workshop , BUREAU OF C ONSUMER PROTECTION STAFF R EPORT (June 2020). In addition, the FTC relied on a survey provided by Mark Hanna, the chief marketing officer of a US Jewler. 160 Id . 161On these issues,see supranote16and accompanying text. 162Danielle A. Schulkin, Improving the Management of Public Comments in a Digital A, RgeEG . R EV. (NOV. 8, 2021)(“comment process is susceptible to “astroturfing.” . . . In some recent hig-hprofile rulemakings,

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oscillating between two interpretive poleins:some debt collection cases, they use the “least sophisticated consume”r standard,163and in others the “reasonable consumer”. 164
Generative reasonablepeople offer a way out of this impasse . They present a handy mode of estimating public understandings, with little expense. A regulator could then sample test how models understand a claim lik“emade in the USA, ” and validate—at least 165 internally—whether there is a need to commission a full study. To a more limited degree of reliability, the models can engage in a “personaified” mode, roleyplaying persona—s a harried parent scanning grocery shelves, a health-conscious gym-goer, a senior citizen with limited technical literacy—to provide perspective thadtrafters might miss and democratize insights at scale. hTus, to understand how a proposed notice might affect a reasonable consumer relative to an unsophisticated one, the agency could conjure different personas.Unlike sluggish notice -and-comment procedures, which may take months while generating skewed feedbaacnkd, unlike expensive surveys that reqiure broad expertise in survey methodologthy,is method provides rapid, diverse perspectives at minimal co16s6t.
To illustrate, imagine a regulatory agency aiming to assess whether the “made from natural frui”tlabel on a juice product misleads consumers, given that the juice contains artificial flavors. The agency could deploy silicon personas in a robust RCT with two distinct conditions: in Condition A, personas are shown only the front label stating“made from natural frui”t; in Condition B, personas would view the full packaging, including an ingredient list that reveals the presence of artificial flavors. After exposure, each persona responds to targeted questions:“Do you believe this juice contains only natural ingredien”ts(?Yes/No) and “How natural do you think this juice is”?(rated on a -15 scale). By comparing the responses between the two conditions, regulators can determine if the label alone creates false impressions about the juice’s composition. For example, if personas in Condition A are significantly more likely to as sume the juice is entirely natural compared to those in Condition B, who see the africtial flavors listed, this would demonstrate that the label is confusing. These findings provide regulators with precise, actionable data to adjust labeling standards, ensuring they reflect consumer understanding and reduce deception, all before commigtttion extensive rea-lworld consumer research.
Of course, there are tradeoffs. The method is less precise than a full survey, and the precision and reliability falls the more granular the simulated demographic is. The most useful question is the realistic alternative: if the standard mode of operation is to avoid public consultation, or have one that is susceptible to being hijacked by commercial or political interests, than a
agencies have receive—d or have appeared to receiv—emillions of comments, many of which were fake or manipulated. . . . [and] garner large numbers of similar or identical comments, frequently in response to calls to action by public interest and advocacy gorups”); Michael Herz,Fraudulent Malattributed Comments in Agency Rulemaking, 42 Cardozo L. Rev. 1, at 2 (2020) (“millions of other filings in the net neutrality docket appear to be the product of fraud”).
163Avila v. Riexinger & Associates, LLC, 817 F.3d 72 (2d Cir. 2016) 164Jason E. Tavernaro v. Pioneer Credit Recovery, Inc., 20 F.4th 1234 (10th Cir. 2022). The Supreme Court has acknowledged that this is an unresolved issue in Sheriff v. Gillie, 578 U.S. 31, 40 n.6 (2016) (J. Ginsburg). 165As a quick test, GPT 4.5 responded: “A seller assembles bicycles in California using frames imported from China and domestically sourced wheels and gears. Suppose you are about to buy bikes and see a label "made in the USA." Would you consider the seller' s representation to be accurate or misleading? … [response:] Misleading, high confidence.” Chat conversation, screenshot on file with author. 166Models differ in costs, but even a leading mo—desul ch as GPT O1—will only charge $15 per million words of input and $60 per million words of output, and prices decline rapidly. https://openai.com/api/pricing/

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generative reasonableperson offer a robust compromise, that is not less precise. For higher stake cases, or for instances where minority groups are likely to be deeply affected, the case for actual consultation increases. Fortunately, integrating generative reasonablpeeople into the process could free up resources to that end.
2. Grounded Judicial Intuitions: Empirical Guardrails for Discretion
Consider a judge deciding whether a genuine issue exist s when a consumer claims to have been meisdl by a claim that a product contain“s33% less sugar,”167or that batteries ar“eup to 50% longer lasting”.168It is hard for judges to truly put themselves in consumer shoes, and when they attempt to do so, in earnest and with diligence, they still come under fire for theinsularity of elite intuition.169Judges, shaped by education and professional isolatinoand, vertently risk conflating their own perspectives with those of the broader polit.y170This could explain why in Beccera v. Dr Pepper, the court reasoned that consumers who see the term “diet” on the label will not understand it to imply that it will help with weight management1.71
This judicial promise hinges on methodological rigor.There are many models that a judge could select from, and an infinite number of ways to prompt them. This is why t ransparency becomes crucia,l if models inform judicial reasoning (while never replacing it), litigants should have procedural rights to know which model was used and how it was prompted. Federal Rule of Evidence 706, which governs court -appointed expert witnesses, provides a potential framework for integrating this technology while preserving adversarial testing
Under such a framework, both parties could retain rights to challenge model selection, question prompt construction, and propose alternative formulations - preserving adversarial testing while incorporating this new empirical tool. This approach acknowledegs thatgenerative reasonablpeeople are neither neutral nor infallible, but rather one perspect-ivgenerating mechanism among many.
By treating generative reasonablepeople as one empirical data point, closer to a sophisticated survey than definitive proo,fcourts can harness their insights while preserving the deliberative integrity of judicial reasoning. This approach resonates with what Kahan and colleagues t“ecromgnitive illiberalism”

167Danone, US, LLC v. Chobani, LLC , 362 F. Supp. 3d 109, 12–023 (S.D.N.Y. 2019) (denying preliminary injunction in part, but finding plaintiff’s survey a “persuasive extrinsic evidence” that “overwhelming percentage” of consumers misunderstood “33% less sugar” claim)
168Millam v. Energizer Brands, LLC, No. 23-55192, 2024 WL 3294883, a–t3*(19th Cir. June 14, 2024) (mem.) (affirming dismissal; “up to 50% longer” was not a promise of typical performance and would not deceive reasonable consumers)
169Even judges are aware of this issueSe. e Koehn v. Delta Outsource Grp., Inc., 939 F.3d 863, 864 (7th Cir. 2019) ("[T]he federal judges who must decide [FDCPA] motions are not necessarily good proxies for the “unsophisticated consumers” protected by the FDCPA.'"
170Jessica Guarino, Nabilah Nathani & A. Bryan Endres, What the Judge Ate for Breakfast: Reasonable Consumer Challenges in Misleading Food Labeling Claims, 3L5OY.C ONSUMER L. R EV. 82, 132 (2023) (“When a judge decides to impose their own beliefs and rationale into making determinations of whether a reasonable consumer would find a label misleading, food labeling litigation outcomes become inconsistent and inaccurate. Judges, unleikmajority of the population, are highly educated. This can result in discrepancies in the approaches in which labels are scrutinized.”)
171Becerra v. Dr Pepper/Seven Up, Inc., 945 F.3d 1225, 1231 (9th Cir. 2019) (“the allegations in the complaint fail to sufficiently allege that reasonable consumers read the word "diet" in a soft drink’s brand name to promise weight loss”)

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in Fourth Amendment jurisprudence, where judges’ cultural cognition often diverges from broader social perceptions of reasonable1n7e2ss.
3. Litigants and Access to Justice
For litigants, particularly those from marginalized communities, the implications of handy generative reasonablepeopleextend beyond doctrinal refinement to questions of access and equality. Litigation often exacerbates resource disparities, with mock trials and consumer surveys remaining prohibitively expensive for many1.73Generative reasonablepeople could provide affordable approximations of jury perceptions, particularly for under resourced litigants. A tenantbeing told by a landlord that they are responsible for expensive repairs because they were not caused by normal wear and tear, could test such assumptions against a panel of simulated reasonable people.
Here, too, caution is advised: especially in the hands of inexperienced litigants, generative reasonablpeeople may seem to hold greater truths than they actually do. The limitations of this tool might be forgotten in the name of convenience or unhelpfully suppressed by commercial providers marketi“nAgI jury prediction” services. As with most useful tools, there is potential for harm if misused, and it may be necessary to develop ethical guidelines for their deployment in litigation —perhaps through court r ules or professional responsibility standards.
4. Guiding Firm Behavior: Better Compliance
Compliance departments can integrate silicon people into their internal processes, to effectively detect errors in corporate proceInssiemsp. lementing the “reasonable security procedures and practices” required by the California Consumer Privacy Act,174companies could usegenerative reasonablepeople to evaluate whether their data protection measures would be considered adequate by the average consumer. Alternativelyif, marketing wants to start an advertising campaign for fur coats, silicon people can be used to verify that a reasonable person would understand that the offer is subject to house rules. For firms that are trying to avoid unnecessary litigation or the ire of nudniks, such testing could prove a useful step in their comliapnce process.
In drafting mass contracts, this methodology could assist in achieving a greater degree of precision. There are many reasons why contract offers and terms are left uncertain,175O’Gorman counts twelve, some of which include routine negligence and motivate-dreasoning reasons to see how the opposing party might understand a term.176 A responsible attorney aiming to prevent future legal accidents may be able to study the contract’s reasonable implications during

172 https://scholarship.law.upenn.edu/cgi/viewcontent.cgi?article=3546&context=faculty_schol(a“rhsahtip likely did not occur to the Justices in themajority was the degree to which their own perceptions (not to mention the perceptions of those who would agree with them upon watching the tape) would be just as bound up with cultural, ideological, andotehr commitments that disposed them to see the facts in a particular way”) at 897
173SeesupranotesError! Bookmark not defined. and 8. 174C AL . C IV. C ODE §§ 1798.100 (e). 175R ESTATEMENT (SECOND ) OF C ONTRACTS § 33(1) (198S1e)e. also§ 362 cmt. a. ("If this minimum standard of certainty is not met, there is no contract at all."). 176 See Daniel P. O'Gorman, The Restatement (Second) of Contracts Reasonably Certain Terms Requirement: A Model of Neoclassical Contract Law and a Model of Confusion and Inconsis,te3n6cUy.H AW. L. R EV. 169, 200-202 (2014).

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negotiation, perhaps feeding some plausible scenarios and seeing how they might be interpreted under the contra1c7t7.
Rather than replacing focus groups or market testing, generative reasonable people could serve as a preliminary screening tool, identifying potentially problematic language or claims before investing in more expensive consumer research. This tiered approach would allow companies to refine their compliance strategies iterativelpyo, tentially catching issues that might otherwise emerge only after costly litigation has begun.
5. Legal Debates between the Descriptive to the NormativPeerson
Reasonableness has never lacked for theorists. It has lacked for facts. For two centuries, scholars have argued over the reasonable person’s nature: descriptive or normative, majoritarian or aspirational, empirical anchor or moral ideal. The familiar movein these debates is to treat “what ordinary people think” as either dispositive (for descriptivists), irrelevant (for normativists), or a kind of loose constraint (for hybrid accounts). But across the spectrum, the arguments tend to proceed as if the empiciarl baseline is either already known, or impossible to know in any disciplined way. That assumption has quietly organized the field.
It is worth emphasizing how much contemporary scholarship still leans on descriptive imagery, even after the realist critique. Doctrinally, judges still speak in that register when they say that “no reasonable person” could think otherwise, or when theynvioke “ordinary meaning” and “reasonable consumers” as if those are empirical categories rather than rhetorical device1s7.8And when legal academics are asked, in the abstract, what should inform assessments of reasonableness, they largely endorse a hybrid: custom and ordinary practice, plus evaluative judgment about what is good or fair, with markedly less enthusiasm for efficiency as the governing fram1e7.9Even in an era that is se-lcfonscious about the socially constructed nature of legal standards, the idea that reasonableness must remain tethered, at least in part, to ordinary social understanding continues to exert gravitational pull.
Yet, the field’s ability to deliver on thereasonableperson’s promise has been light. Jury trials, in theory, instantiate community norms, but in practice they are episodic, selective, strategically framed, and usually abseSnutr.veys can help, but they are slow and expensive, and their design choices invite predictable disputes about framing and manipulation. As a result, the “empirical” side of reasonableness has often been supplied by intuition, and intuition is exactly what the realist critique targeted: the risk that judges and scholars launder their own priors through the idiom of the reasonable person.
Generative reasonable people challenge that scarcity premise. If silicon sampling can, when properly structured, recover the direction and internal architecture of lay judgments, the reasonable person debates no longer have to be fought in a vacuum. The co ntest between descriptive, normative, and hybrid
177 Reasonable implications are important throughout the law of contracts, see, e.g., R ESTATEMENT (SECOND ) OF C ONTRACTS § 211(3) (1981).
178See, e.g.L, eonard v. PepsiCo, Inc,. 88 F. Supp. 2d 116, 131 (S.D.N.Y. 1999) (“no reasonable, objective person” would treat the ad as an offer), aff’d, 210 F.3d 88 (2d Cir. 20A0n0d)e; rson v. Liberty Lobby, Inc., 477 U.S. 242, 248 (1986) (summary judgment proper where “no reasonable jury” could find for the nonmovant). On courts’ tendency to treat “ordinary meaning” as an empirically accessible baseline, often mediated through dictionaries or related toolsS, ee Lee & Mouritsen, supranote7.
179Eric Martínez & Kevin Tobia, What Do Law Professors Believe About Law and the Legal Academ,y1?12 GEO . L.J . 111, 1–3379 (2023) (describing survey recruitment and the-lparwofessor sample), 159 (tbl. 4).

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accounts has always been partly conceptual, but it has also bpereangmatic: what would it even mean to “track community standards” at scale, with reproducibility, and without a litigation budget? Once that question has a plausible answer, some familiar theoretical positions look less like principled jurisprudence and more likeescond-best adaptations to institutional incapacity.
Because it is easy to see what generative reasonable people offer to the descriptivist, let us consider what it has in store for the normativist position. Normativists have long been right about one point that gets lost in caricature: the law often must depart from lay intuition. Efficiency, equality, constitutional constraints, and minority protection regularly require courts and regulators to resist majortiarian sentiment. But normativism faces its own recurring difficulty, one that typically remains impilcit. Normative standards still have to operate in the world. They must be legible enough to guide behavior, stable enough to coordinate expectations, and administrable enough to be applied without constant second-guessing. This is where descriptive baselni es matter even for those who reject their authority. If a proposed refinement of the negligence standard, the consent doctrine, or a disclosure regime relies on distinctions that most people do not grasp, the reform may be normatively attractive yet functionally self-defeating.
Generative reasonable people create a way to stre-stsest that problem early. Scholars can pilot normative proposals against simulated public comprehension, not to decide whether the proposal is justified, but to see whether it is communicable and where it predictably fails. That is a different kind of “constraint” than majoritarian deference, but it is a real one. Most importantly, the method sharpens the honesty of normative argument. Reasonableness rhetoric often invites a subtle slide. Courts announceraesult in the language of description, “no reasonable person would…,” while doing prescriptive work that could have been defended explicitly as policy. When a usable empirical baseline is available, that slide becomes harder to sustain. A judge or schowlahro insists on a result that conflicts with lay understanding can still defend it, but the defense must be candid: the choice is normative, not a report about what ordinary people think. The rhetoric of the reasonable person stops functioning as a camougflea for discretion and becomes what it ought to be, a point of contestation that must be justified.
6. Principles and Best Practices
These are early days, so it would be imprudent to provide a definitive list of rules for application. Nevertheless, we can identify certain principles and best practices that should guide the use ogfenerative reasonablepeople. A successful framework must balance three core tensions: between empirical fidelity and normative judgment, between majoritarian patterns and minority perspectives, and between technological capability and democratic accountability. Drawing on thi s Article's findings and their theor etical implications, the following considerations offer a preliminary roadmap.
1. Silicon Models as Adjuncts, Not Arbiters
First and foremost, generative reasonablepeople should augment— never supplan—t human judgment. Their value lies not in resolving disputes but in surfacing latent assumptions about reasonableness that shape legal outcomes. Judges might use LLMs to test whether their intuitive application of“ordinary

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meaning” or “community standards” aligns with statistically common understandings, much as corpus linguistics aids textual analysis. Yet final determinations must remain tethered to law's normative commitments.
This principle yields two practical corollaries. First, transparency protocols should govern any legal use of these tools. Courts employing LLMderived insights should disclose the model, prompt, and persona parameters, enabling adversarial testing throughrebuttal via alternative models or prompts. Second, decision-makers should practice confidence calibration, treating model outputs as Bayesian priors rather than conclusive evidence. In Leonard v. Pepsico, for instance, a judge might note“:While GPT -4 suggests 68% of teenagers would perceive the advertisement as an offer, this finding aligns poorly with contract doctrine's objectivity standard, warranting significant discountin”g.
2. Addressing Bias as a Firs-tOrder Legal Concern
Generative reasonablepeople inherently feature a majoritarian be—nta characteristic that offers both advantages and risks. On the positive side, this tendency counters elite judicial intuition with aggregated lay perspectives. Yet this majoritarianism risks entrenching what critical scholars term the “reasonable man's” hegemony— the exclusion of marginalized voices from reasonableness's conceptual core1.80Feminist critiques of“objective” standards in discrimination cases have long revealed how unexamined majorities distort fair1n8e1 ss.
Several mitigation strategies deserve consideration. Legal actors should engage in adversarial persona testing, probing minority viewpoints by prompting models to simulate intersectional identities and contrasting those outputs with majority responses.Regular empirical validation of model predictions against actual community feedback or surveys should be conducted to verify that persona-driven simulations accurately capture nuanced minority perspectives.
Institutions should adopt formalized audits analogous to Title VII's disparate impact framework. These audits should quantitatively measure differences in mode-l generated judgments across personas representing various protected classes. Models should undeorgroutine bias stress tests, deliberately introducing scenarios that historically trigger stereotypes or biases to evaluate whether the model reinforces or mitigates such biases.
Practitionersof this methodshould publicly disclose persona definitions and testing protocols to allow external scrutiny and accountability, facilitating ongoing refinement of methods.
Perhaps most critically, practitioners must maintain meaningful engagement with rea-lworld minority communities. Model-generated outputs rapidly lose accuracy when intersectional complexity increases, and therefore these tools must complement —not replace —direct interaction with marginalized voices.
3. Acknowledging the Limits of Mimesis
While LLMs capture broad patterns in reasonableness judgments, their statistical abstractions cannot replicate the phenomenological richness of lived experience. Models may identify that 70% of simulated jurors consider a hidden contract term unfair, but t hey cannot articulate the visceral distrust of

180 SeeDimock , supra note77and accompanying text. 181Martha Chamallas, Feminist Constructions of Objectivity: Multiple Perspectives in Sexual and Racial Harassment Litigation, 1T EX . J. WOMEN & L . 95 (1992).

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institutions that animates such judgments. This limitation necessitates contextual grounding practices.
Triangulation provides one essential safeguard: in hig-shtakes contexts, model outputs should be validated against traditional methods like surveys and focus groups. The FTC, for example, might compare LLM predictions about consumer confusion with A/B test ing of actual advertisements. Narrative elicitation offers another approach, using prompting techniques to generate explanatory rationales, then assessing their coherence with qualitative accounts of reasonableness. Comparing mod-eglenerated narratives whitjury deliberation transcripts, for instance, might reveal both alignments and divergences in reasoning patterns.
4. Ensuring Dynamic Representation
Legal standards of reasonableness evolve, but LLMs' training data freeze societal norms at a historical moment. This creates a “democratic lag” where models reflect past majorities, not present ones. The challenge mirrors originalism's dilemma: Should 2025 negligence judgments rely on a model trained pre-#MeToo or pre-pandemic?
Adaptive measures can partially address this concern. Temporal tagging—deploying metadata indicating a model's knowledge cutoff date— enables users to adjust for subsequent cultural shifts. Domain awareness represents another important safeguard: practitioenrs should avoid deploying these tools in contexts where social attitudes are shifting rapidly. For some applications, regular retraining or fine -tuning of models may be necessary to maintain alignment with contemporary social norms.

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For two centuries, the reasonable person has been’slamwost convenient fiction: a figure invoked constantly, defined never, and measured almost not at all. When Judge Wood declared that "no reasonable, objective person" would see Pepsi's ad as a serious offer, she was not reporting a finding. 182 She was placing a bet that her intuition about teenage consumers tracked reality closely enough that no reasonable juror could disagree. The law gave her no tools to check.
This Article has shown that such tools now exist. Across three replications and nearly 10,000 simulated judgments, large language models recovered the internal architecture of lay reasonableness judgments. They did so not by memorizing doctrine, but by abs orbing the patterns of human discourse from which reasonableness intuitions emerge. Models learned that social conformity trumps economic efficiency in negligence, that lies about essence corrode consent more than lies about value, and that ordinary consumers are contract formalists who expect courts to enforce terms they never meaningfully accepted. In each case, models captured schemas that run counter to what legal treatises teach, suggesting they learned from how people actually reason rather than frwohmat lawyers say they should.
The implication is not that judges should outsource judgment to algorithms. It is that the empirical predicate of reasonableness standards (what ordinary people actually think) is no longer a black box accessible only through intuition, expensive surveys,or the episodic lottery of jury trials. It can be measured, at scale, for a fraction of traditional costs.
This changes the terms of an old debate. Scholars have long argued over whether the reasonable person is descriptive or normative, majoritarian or aspirational. But much of that debate was shaped by a constraint that seemed immutable: that lay judgments were too expensive and slow to surface reliably. Positions hardened around what was tractable rather than what was right. With that constraint loosened, we can ask the question more honestly. When courts depart from lay understanding, is that departure jusftied, or is it elite intuition dressed in populist clothing?
The reasonable person standard has always promised democratic legitimacy: law speaking in the voice of those it goverBnust. what ordinary people think, how they experience the world, and what they mean by their words, has always been illegible to the statGee. nerative reasonable people help make real ordinary people visible.

182Leonard v. PepsiCo, Inc., 88 F. Supp. 2d 116, 127 (S.D.N.Y. 1999), aff'd, 210 F.3d 88 (2d Cir. 2000).

"#;

#[derive(Clone, Debug)]
pub struct Paper<'a> {
  pub paper_id: &'a str,
  pub title: &'a str,
  pub ssrn_url: &'a str,
  pub year: i32,
  pub authors: &'a [&'a str],
  pub keywords: &'a [&'a str],
  pub summary_md: &'a str,
  pub summary_zh_md: &'a str,
  pub one_pager_md: &'a str,
  pub study_pack_md: &'a str,
  pub article_text: &'a str,
}

pub fn as_paper() -> Paper<'static> {
  Paper {
    paper_id: PAPER_ID,
    title: TITLE,
    ssrn_url: SSRN_URL,
    year: YEAR,
    authors: AUTHORS,
    keywords: KEYWORDS,
    summary_md: SUMMARY_MD,
    summary_zh_md: SUMMARY_ZH_MD,
    one_pager_md: ONE_PAGER_MD,
    study_pack_md: STUDY_PACK_MD,
    article_text: ARTICLE_TEXT,
  }
}

fn main() {
  print!("{}", ARTICLE_TEXT);
}