204 pages

Published April 21, 2026 by The MIT Press.

ISBN:
978-0-262-05173-6
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Our lives are increasingly governed by automated systems influencing everything from medical care to policing to employment opportunities, but researchers and investigative journalists have proven that AI systems regularly get things wrong.

Auditing AI is a first-of-its-kind exploration of why and how to audit artificial intelligence systems. It offers a simple roadmap for using AI audits to make product and policy changes that benefit companies and the public alike. The book aims to convince readers that AI systems should be subject to robust audits to protect all of us from the dangers of these systems. Readers will come away with an understanding of what an AI audit is, why AI audits are important, key components of an audit that follows best practices, how to interpret an audit, and the available choices to act on an audit’s results.

The book is organized around canonical examples: from AI-powered drones mistakenly …

2 editions

A Solid Introduction

This authors list is a veritable who's-who of the ethical AI space, and this shows in the straightforward, insightful approach they take to working through the ins and outs of the AI auditing process. Most of the cases presented here will be familiar to anyone who's already in the space, and if you've read some of the literature you'll probably be most interested in the more practical discussions of AI auditing, although those sections are fairly surface level due to the length of the book. I wish there was more time spent on identifying auditing targets and the implications of choosing easier to audit systems/small players in the space versus more substantive but potentially challenging targets. I'm biased since I know pymetrics' founder well, but the section on that was weirdly unfair criticism given the legal limits of the HR space (unsure if the authors have experience there), and the …

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