Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrines
This work addresses the challenge of developing more holistic and morally grounded fairness definitions for algorithmic systems, which is an incremental step in the field of AI ethics.
The paper tackles the problem of defining algorithmic fairness by using Equal Opportunity doctrines from political philosophy to highlight the normative judgments in different fairness conceptions, contrasting formal and substantive approaches. It interprets impossibility results as conflicts between forward-looking and backward-looking fairness when life chances are unequal, and proposes two procedures based on luck-egalitarian and Rawlsian principles.
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.