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Efforts to promote equitable public policy with algorithms appear to befundamentally constrained by the "impossibility of fairness" (anincompatibility between mathematical definitions of fairness). This technicallimitation raises a central question about algorithmic fairness: How cancomputer scientists and policymakers support equitable policy reforms withalgorithms? In this article, I argue that promoting justice with algorithmsrequires reforming the methodology of algorithmic fairness. First, I diagnosethe problems of the current methodology for algorithmic fairness, which I call"formal algorithmic fairness." Because formal algorithmic fairness restrictsanalysis to isolated decision-making procedures, it leads to the impossibilityof fairness and to models that exacerbate oppression despite appearing "fair."Second, I draw on theories of substantive equality from law and philosophy topropose an alternative methodology, which I call "substantive algorithmicfairness." Because substantive algorithmic fairness takes a more expansivescope of analysis, it enables an escape from the impossibility of fairness andprovides a rigorous guide for alleviating injustice with algorithms. In sum,substantive algorithmic fairness presents a new direction for algorithmicfairness: away from formal mathematical models of "fair" decision-making andtoward substantive evaluations of whether and how algorithms can promotejustice in practice.

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