Fairness and Friends

Falaah Arif Khan, Eleni Manis, Julia Stoyanovich

Keywords: fairness, fair machine learning, equality of opportunity, algorithmic justice, comic book, RDS comic, responsible AI, data responsibly comic book

TL;DR: "Fairness and Friends" is the second volume of the "Data, Responsibly" comic series, covering issues of bias in algorithmic systems, fairness in machine learning and broader doctrines of equality of opportunity and justice from political philosophy.

Abstract:

Recent interest in codifying fairness in Automated Decision Systems (ADS) has resulted in a wide range of formulations of what it means for an algorithm to be “fair.” Most of these propositions are inspired by, but inadequately grounded in, scholarship from political philosophy. This comic aims to correct that deficit. We begin by setting up a working definition of an 'Automated Decision System' (ADS) and explaining 'bias' in outputs of an ADS. We then critically evaluate different definitions of fairness as Equality of Opportunity (EOP) by contrasting their conception in political philosophy (such as Rawls’s fair EOP and formal EOP) with the proposed codification in Fair-ML (such as statistical parity, equality of odds and accuracy) to provide a clearer lens with which to view existing results and to identify future research directions. We use this framing to reinterpret the impossibility results as the incompatibility between different EOP doctrines and demonstrate how political philosophy can provide normative guidance as to which notion of fairness is applicable in which context. We conclude by highlighting justice considerations that the fair-ML literature currently overlooks or underemphasizes, such as Rawls's broader theory of justice, which supplements his EOP principle with a
principle guaranteeing equal rights and liberties to all citizens in a free and democratic society.