Practical Steps for Building Fair AI Algorithms (Coursera)

This course is aimed at a broad audience of students in high school or above who are interested in computer science and algorithm design. It will not require you to write code, and relevant computer science concepts will be explained at the beginning of the course. The course is designed to be useful to engineers and data scientists interested in building fair algorithms; policy-makers and managers interested in assessing algorithms for fairness; and all citizens of a society increasingly shaped by algorithmic decision-making.

What you'll learn

- Understand widely used definitions of fairness and bias

- Master principles to follow when training models

- Design a healthcare algorithm

- Reason about challenging algorithmic fairness dilemmas

Syllabus

Introduction

In this module, you'll learn the basic concepts this course relies on: what an algorithm is, and why fairness is tricky and subtle to define. We'll start by defining what a predictive algorithm even is, because this course is designed to be accessible to students who have never taken a computer science class. (If you have taken a previous class on predictive algorithms or machine learning, feel free to skip this section.) Then we'll jump right into fairness. This course will present ten practical fairness lessons, and in this module we'll discuss two of them. We'll also give a sneak preview of how the lessons of this course apply to generative AI models like ChatGPT.

Designing Algorithms

This module will cover fundamental lessons for designing fair algorithms: what data they should be trained on, what features they should use to predict, and what outcomes they should predict.

Documenting Algorithms

This module discusses the importance of documenting algorithms and datasets so they are used only in settings where they are appropriate.

Algorithms in the hands of humans

This module discusses the complex interplay between algorithmic predictions and human decisions.