In this work we use Equal Opportunity (EO) doctrines from political philosophy to make explicit the normative judgements embedded in different conceptions of algorithmic fairness. We contrast formal EO approaches that narrowly focus on fair contests at discrete decision points, with substantive EO doctrines that look at people’s fair life chances more holistically over the course of a lifetime. We use this taxonomy to provide a moral interpretation of the impossibility results as the incompatibility between different conceptions of a fair contest — foward-facing versus backward-facing — when people do not have fair life chances. We use this result to motivate substantive conceptions of algorithmic fairness and outline two plausible fair decision procedures based on the luck egalitarian doctrine of EO, and Rawls’s principle of fair equality of opportunity.
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Fairness and Friends
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.
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- NSF-PAR ID:
- 10398898
- Date Published:
- Journal Name:
- Beyond static papers: Rethinking how we share scientific understanding in ML - ICLR 2021 workshop
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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