A Philosophical Theory of Fairness for Prediction-Based Decisions.

39 Pages Posted: 18 Sep 2019 Last revised: 6 Feb 2022

See all articles by Michele Loi

Michele Loi

Department of Mathematics, Politecnico di Milano

Anders Herlitz

The Institute for Future Studies

Hoda Heidari

Carnegie Mellon University

Date Written: September 9, 2019

Abstract

This paper presents a fairness principle that can be used to evaluate decision-making based on predictions. We propose that a decision rule for decision-making based on predictions is fair when the individuals directly subjected to the implications of the decision enjoy fair equality of chances. We define fair equality of chances as obtaining if and only if the individuals who are equal with respect to the features that justify inequalities in outcomes have the same statistical prospects of being benefited or harmed, irrespective of their morally irrelevant traits. The paper characterizes – in a formal way – the way in which luck is allowed to impact outcome in order for its influence to be fair. This fairness principle can be used to evaluate decision-making based on predictions, a kind of decision-making that is becoming increasingly important to theorize around in light of the growing prevalence of algorithmic decision-making in healthcare, the criminal justice system and the insurance industry, among other areas. It can be used to evaluate decision-making rules based on different normative theories, and is compatible with the broadest range of normative views according to which inequalities due to brute luck can be fair.

Keywords: Fairness, bias, statistical decision-making, statistical discrimination

Suggested Citation

Loi, Michele and Herlitz, Anders and Heidari, Hoda, A Philosophical Theory of Fairness for Prediction-Based Decisions. (September 9, 2019). Available at SSRN: https://ssrn.com/abstract=3450300 or http://dx.doi.org/10.2139/ssrn.3450300

Michele Loi (Contact Author)

Department of Mathematics, Politecnico di Milano ( email )

Milano, CH-8006
Italy

Anders Herlitz

The Institute for Future Studies ( email )

Holländargatan 13
Stockholm, 11136
Sweden

Hoda Heidari

Carnegie Mellon University ( email )

Machine Learning Department
5000 Forbes Avenue Gates Hillman Center, 8th Floor
Pittsburgh, PA 15213
United States

HOME PAGE: http://www.cs.cmu.edu/~hheidari/

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