Lecture 11: Ethics and Bias

Slides

Link for the slides.

Today’s Topics: Ethics, Bias and the Law

• Introduction to ethics in AI and computer science
• Definitions and introduction to bias.
• Some technical discussions on how to avoid bias.
• Introduction to EU laws on AI.

Reading Guide

There are a lot of links in the slides (email me if they are broken). You are encouraged to read them. The whole EU AI Act is long and written for lawyers. You should read Ethics guidelines for trustworthy AI instead.

You should also read chapter 13 of A Hands-On Introduction to Machine Learning.

What should I know by the end of this lecture?

• Definitions of Bias
• Understand basic ethical problems within machine learning and AI.
• Understand technical solutions to bias and the limitations.
• Some understanding of the AI framework for AI, and what it means to you as a future machine learning engineer.

This material might be examined (I haven’t examined it in previous years, this material was new in 2024). One way of examining the material is to present a simple case study, and ask you to discuss bias and how the AI framework would have an impact on you as a machine learning engineer. A good place to start would be to think about the Amazon recruiting system.