Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrines

TL;DR

This paper uses political philosophy doctrines to analyze and interpret different conceptions of algorithmic fairness, emphasizing the importance of holistic, lifetime-based fairness over narrow, decision-point-focused approaches.

Contribution

It introduces a normative framework based on equal opportunity doctrines to distinguish between formal and substantive fairness in algorithms, providing moral interpretations of impossibility results.

Findings

Formal EO approaches focus on decision points.

Substantive EO considers lifetime fairness.

Impossibility results relate to conflicting fairness conceptions.

Abstract

In this work we use Equal Oppportunity (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-looking versus backward-looking -- when people do not have fair life chances. We use this result to motivate substantive conceptions of algorithmic fairness and outline two plausible procedures based on the luck-egalitarian doctrine of EO, and Rawls's principle of fair equality of opportunity.