Fairness and machine learning

Limitations and Opportunities

Solon Barocas, Moritz Hardt, Arvind Narayanan

EmphBox.

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.

Table of contents

About this book

  1. Introduction
  2. Demographic classification criteria

We introduce formal non-discrimination criteria, establish their relationships, and illustrate their limitations.

  1. Legal background and normative questions

We survey the literature on discrimination in law, sociology, and philosophy. We then discuss the challenges that arise in translating these ideas of fairness to the statistical decision-making setting.

  1. Causal inference

We dive into the rich technical repertoire of causal inference and how it helps articulate and address shortcomings of the prediction paradigm.

  1. Measurement

Considerations of fairness begin with measurement, the process of assigning empirical relationships to numerical representations.

  1. Algorithmic interventions

We survey and systematize a burgeoning set of algorithmic interventions aimed at promoting fairness, while highlighting the limitations of this paradigm.

  1. Related concepts

We discuss privacy, interpretability, transparency, and accountability, and how each of these concepts relates to fairness.

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Contact us

We welcome your feedback. For all book related questions and comments, please send an email to contact@fairmlbook.org.

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 = {2018}
}
Last updated: Tue Aug 21 16:18:20 PDT 2018