Fairness and machine learning

Limitations and Opportunities

Solon Barocas, Moritz Hardt, Arvind Narayanan

This online textbook is an incomplete work in progress. Essential chapters are still missing. In the spirit of open review, we solicit broad feedback that will influence existing chapters, as well as the development of later material. Please contact us at contact@fairmlbook.org.

CONTENTS
Preface
Acknowledgments
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 introduce formal non-discrimination criteria in a decision-theoretic setting, establish their relationships, and illustrate their limitations.
5 Causality PDF
We dive into the rich technical repertoire of causal inference 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 Algorithmic interventions
In this online-only chapter, we survey and systematize a burgeoning set of algorithmic interventions aimed at promoting fairness, while highlighting the limitations of this paradigm.

Video tutorials

Course materials

Citations

To cite this book, please use this bibtex entry:

@book{barocas-hardt-narayanan,
  title = {Fairness and Machine Learning},
  author = {Solon Barocas and Moritz Hardt and Arvind Narayanan},
  publisher = {fairmlbook.org},
  note = {\url{http://www.fairmlbook.org}},
  year = {2019}
}
Last updated: Tue Aug 2 04:45:10 PDT 2022