Citation

Escaping the Impossibility of Fairness: From Formal to Substantive Algorithmic Fairness

Author:
Green, Ben
Publication:
Philosophy & Technology
Year:
2022

Efforts to promote equitable public policy with algorithms appear to be fundamentally constrained by the ā€œimpossibility of fairnessā€ (an incompatibility between mathematical definitions of fairness). This technical limitation raises a central question about algorithmic fairness: How can computer scientists and policymakers support equitable policy reforms with algorithms? In this article, I argue that promoting justice with algorithms requires reforming the methodology of algorithmic fairness. First, I diagnose the problems of the current methodology for algorithmic fairness, which I call ā€œformal algorithmic fairness.ā€ Because formal algorithmic fairness restricts analysis to isolated decision-making procedures, itĀ leads to the impossibility of fairness and to models that exacerbate oppression despite appearing ā€œfair.ā€ Second, I draw on theories of substantive equality from law and philosophy to propose an alternative methodology, which I call ā€œsubstantive algorithmic fairness.ā€ Because substantive algorithmic fairness takes a more expansive scope of analysis, it enables an escape from the impossibility of fairness and provides a rigorous guide for alleviating injustice with algorithms. In sum, substantive algorithmic fairness presents a new direction for algorithmic fairness: away from formal mathematical models of ā€œfairā€ decision-making and toward substantive evaluations of whether andĀ how algorithms can promote justiceĀ in practice.