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

As governments embrace algorithms, the burgeoning field of algorithmic fairness provides an influential methodology for promoting equality-enhancing reforms. However, even algorithms that satisfy mathematical fairness standards can exacerbate oppression, causing critics to call for the field to shift its focus from "fairness" to "justice." Yet any efforts to achieve algorithmic justice in practice are constrained by a fundamental technical limitation: the "impossibility of fairness" (an incompatibility between mathematical definitions of fairness). The impossibility of fairness thus raises a central question about algorithmic fairness: How can computer scientists 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 why the current methodology for algorithmic fairness--which I call "formal algorithmic fairness"--is flawed. I demonstrate that the problems of algorithmic fairness--including the impossibility of fairness--result from the methodology of the field, which restricts analysis to isolated decision-making procedures. Second, I draw on theories of substantive equality from law and philosophy to propose an alternative methodology: "substantive algorithmic fairness." Because substantive algorithmic fairness takes a more expansive scope to fairness, 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 "fairness" and toward substantive evaluations of how algorithms can (and cannot) promote justice.