AI Fairness: Designing Equal Opportunity Algorithms

(MIT Press, 2024)

Decisions about important social goods like education, employment, housing, loans, healthcare, and criminal justice are all becoming increasingly automated with the help of AI systems. But because AI systems are trained on data with historical inequalities, many of these systems produce unequal outcomes for members of disadvantaged groups.

For several years now, researchers who design AI systems have investigated the causes of inequalities in AI decisions, and proposed techniques for mitigating them. It turns out that in most realistic conditions it is impossible to enforce equality across all metrics simultaneously. Because of this, companies using AI systems will have to choose which metric they think is the correct measure of fairness, and regulators will need to determine how to apply currently existing laws to AI systems.

This book will draw on traditional philosophical theories of fairness to develop a framework for evaluating these standards and measurements, which can be called a Theory of Algorithmic Justice. The theory is inspired by the Theory of Justice developed by the American philosopher John Rawls. Most books on this topic are written by computer scientists, and this book is unique in bringing important ideas from ethics and political philosophy to provide the arguments that are needed to defend the fairness of an AI system.