Fairness and machine learning

Limitations and Opportunities

Solon Barocas, Moritz Hardt, Arvind Narayanan

1 Introduction PDF
2 When is automated decision making legitimate? PDF
We explore what makes automated decision making a matter of normative concern, situated in bureaucratic decision making and its mechanical application of formalized rules.
3 Classification PDF
We introduce formal non-discrimination criteria in a decision-theoretic setting, establish their relationships, and illustrate their limitations.
4 Relative notions of fairness PDF
We explore the normative underpinnings of objections to systematic differences in the treatment of different groups and inequalities in the outcomes experienced by these groups.
5 Causality PDF
We dive into the technical repertoire of causality and how it helps articulate and address shortcomings of the classification paradigm, while raising new conceptual and normative questions.
6 Understanding United States anti-discrimination law PDF
We discuss what United States anti-discrimination law is and isn’t, how it navigates tradeoffs, its limits, and how it applies to machine learning.
7 Testing discrimination in practice PDF
We systematize tests of discrimination and discuss the practical complexities of applying them, both to traditional decision-making systems and to algorithmic systems.
8 A broader view of discrimination PDF
We review structural, organizational, and interpersonal discrimination in society, how machine learning interacts with them, and discuss a broad set of potential interventions.
9 Datasets PDF
Datasets are the backbone of machine learning research and development. We critically examine their role, the harms associated with data, and survey improvements in data practices.
10 Where to go from here
In this online-only chapter, we survey and systematize recent work on algorithmic fairness.
A Problems
Problem sets, available online

Video tutorials

Course materials

Contact us

We welcome your feedback, questions, and suggestions. You can reach us at contact@fairmlbook.org. If you taught from the book, we’d love to hear about it.

Citations, license, typesetting

To cite this book, please use this bibtex entry:

  title = {Fairness and Machine Learning: Limitations and Opportunities},
  author = {Solon Barocas and Moritz Hardt and Arvind Narayanan},
  publisher = {fairmlbook.org},
  note = {\url{http://www.fairmlbook.org}},
  year = {2019}
  • A hardcover print edition will be published by MIT Press in 2023.

  • The text available on this website is licensed under the Creative Commons BY-NC-ND 4.0 license.

  • This book is typeset using pandoc with the unbuch setup.

Last updated: Mon Nov 7 17:03:56 CET 2022