Here's the bullet list summary for 'ssrn-5181207' by Professor Yonathan Arbel:

1.  ## TL;DR
    Professor Yonathan Arbel of the University of Alabama School of Law argues that a "capability-safety gap" in AI development, where private firms reap rewards while society bears risks, creates a social misalignment. He proposes using tax policy to address this by re-conceptualizing R&D credits to incentivize safety research, offering consumer credits for safe AI, imposing penalties for non-compliance, and redistributing penalty revenue. This approach aims to embed safety imperatives directly into the economic architecture of AI development, aligning private profit with social welfare.

2.  ## Section Summaries

    ## The Capability-Safety Gap and the Case for Tax Intervention
    Professor Yonathan Arbel of the University of Alabama School of Law writes that a dangerous "capability-safety gap" in AI is widening, as developers gain private rewards while society bears cascading risks, creating a social misalignment. Professor Yonathan Arbel of the University of Alabama School of Law writes that fiscal policy, specifically taxation, offers a powerful and adaptable tool to embed safety imperatives into AI's economic architecture, aiming to align private profit with social welfare and address the urgent need for intervention due to traditional regulatory failures and the collapse of industry self-regulation.

    ## Business Tax-Incentives for Investments in AI Safety
    Professor Yonathan Arbel of the University of Alabama School of Law writes that specific business tax-incentives, such as credits or enhanced deductions, should directly encourage substantial corporate investments in AI safety research, development, and deployment. Professor Yonathan Arbel of the University of Alabama School of Law writes that these fiscal mechanisms, including a proposed "AI Safety Research Tax Credit" and adjusted expensing rules favoring safety R&D (like testing and alignment) over pure capability enhancements, aim to make safety-enhancing activities economically attractive, addressing underinvestment by aligning financial interests with societal well-being.

    ## Consumer-Side Incentives and Market-Based Certification
    Professor Yonathan Arbel of the University of Alabama School of Law writes that consumer-side incentives are crucial for fostering a market for safe AI, proposing an "AI Reliability Credit" for consumers purchasing AI products certified as reliable and safe, mirroring energy efficiency tax incentives. Professor Yonathan Arbel of the University of Alabama School of Law writes that this credit would incentivize producers to pursue rigorous safety certifications (e.g., for bias mitigation, data protection), thereby spurring consumer demand for safer AI and aligning private firm incentives with societal goals through market-based certification.

    ## Corrective Taxes and Penalties for Non-Compliance
    Professor Yonathan Arbel of the University of Alabama School of Law writes that to penalize unsafe AI development, corrective Pigouvian taxes should make firms internalize the external harms they create. Professor Yonathan Arbel of the University of Alabama School of Law writes that a comprehensive penalty framework, featuring graduated penalties like tax surcharges and benefit recapture for AI posing public safety risks, aims to internalize social costs, create strong ex ante incentives for responsible innovation, and ensure public funds do not subsidize harmful AI, building on precedents where tax benefits are compliance-contingent.

    ## Administrative Advantages and Challenges of a Tax-Based Approach
    Professor Yonathan Arbel of the University of Alabama School of Law writes that tax policy offers distinctive advantages for AI safety, harnessing existing institutional frameworks like the IRS, preserving market dynamics, and potentially reshaping organizational culture. Professor Yonathan Arbel of the University of Alabama School of Law writes that while challenges include political economy concerns and distinguishing genuine safety from "safety-washing," his framework suggests the IRS leverage its R&D evaluation experience, mandate detailed safety documentation, and use emerging industry benchmarks to address these issues and effectively mobilize private sector expertise for AI safety.