---
title: "EU Ethics Guidelines for Trustworthy AI"
output:
  xaringan::moon_reader:
    css: xaringan-themer.css
    nature:
      ratio: 16:10
---

```{r setup, include=FALSE}
options(htmltools.dir.version = FALSE)
knitr::opts_chunk$set(
  fig.width=9, fig.height=3.5, fig.retina=3,
  out.width = "100%",
  cache = FALSE,
  echo = TRUE,
  message = FALSE, 
  warning = FALSE,
  fig.show = TRUE,
  hiline = TRUE
)
```

```{r xaringan-themer, include=FALSE, warning=FALSE}
library(xaringanthemer)
style_duo_accent(
  primary_color = "#1381B0",
  secondary_color = "#FF961C",
  inverse_header_color = "#FFFFFF"
)
```


 We hear frequently of the challenges with AI and even simple predictive
algorithms share the same issues. Recently, 
The EU came up with a set of
guidelines to help diminish the chance of ethical issues in such
algorithms. 

---

## 1. Human agency and oversight

Users should be able to make informed autonomous decisions regarding AI
systems. They should be given the knowledge and tools to comprehend and
interact with AI systems to a satisfactory degree and, where possible,
be enabled to reasonably self-assess or challenge the system. Humans are
involved in the design and governance of the system including when and
where the system is used.

---

## 2. Technical robustness and safety

Including resilience to attack and security, fall back plan and general
safety, accuracy, reliability with a range of inputs and reproducibility
of results with same inputs. The system is well-tested during creation
and reviewed during use.

---

## 3. Privacy and data governance

Including respect for privacy, quality and integrity of data, and access
to data. Data must be ethically cleaned before it is used.

---

## 4. Transparency

The data sets on which the decision making is based must be
well-documented with regard to origin and alteration (such as in
cleaning). Decisions made by AI must be understandable and able to be
traced. Users should have access to the decision-making process and be
aware that the decision was made, at least in part, but a computer.

---

## 5. Diversity, non-discrimination and fairness

Including the avoidance of unfair bias, accessibility and universal
design, and stakeholder participation. Diversity should be encouraged
and designers should consider carefully whether past positive results
(success of people with certain gender, ethnicity, personality etc.)
were related or unrelated to the success. Avoid propagation of past
bias. Systems should be user-centric and accessible to the widest range
of users. People who will be affected by the system should be involved
throughout the design of the system.

---

## 6. Societal and environmental wellbeing

Including sustainability and environmental friendliness, social impact,
society and democracy. Supply chain for the system should be
sustainable. Its use should impact social relationships in a negative
way. It should enhance, rather than detract from, a democratic society.

---

## 7. Accountability

This necessitates that mechanisms be put in place to ensure
responsibility and accountability for AI systems and their outcomes,
both before and after their development, deployment and use. Potential
impacts should be minimized and when encountered, they should be
addressed and there should be a policy for making amends to the injured
party. The system should be adjusted to eliminate the possibility of
this happening in the future. When a conflict of interest arises, the
tradeoffs made should be acknowledged.