Paradoxes in Fair Computer-Aided Decision Making

TL;DR

This paper reveals fundamental paradoxes in fair computer-aided decision making, showing that achieving fairness often entails discrimination or forces rational decision-makers to discriminate, and characterizes when fairness is possible.

Contribution

It provides a formal analysis of the inherent conflicts in designing fair algorithms and characterizes conditions under which fair decision making can be achieved.

Findings

Either classifiers or decision-makers must be discriminatory in non-trivial cases.

Complete characterization of scenarios allowing fair decision making.

Highlights fundamental limitations in algorithmic fairness.

Abstract

Computer-aided decision making--where a human decision-maker is aided by a computational classifier in making a decision--is becoming increasingly prevalent. For instance, judges in at least nine states make use of algorithmic tools meant to determine "recidivism risk scores" for criminal defendants in sentencing, parole, or bail decisions. A subject of much recent debate is whether such algorithmic tools are "fair" in the sense that they do not discriminate against certain groups (e.g., races) of people. Our main result shows that for "non-trivial" computer-aided decision making, either the classifier must be discriminatory, or a rational decision-maker using the output of the classifier is forced to be discriminatory. We further provide a complete characterization of situations where fair computer-aided decision making is possible.