Efforts to promote equitable public policy with algorithms appear to be fundamentally constrained by the āimpossibility 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 āformal 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 āfair.ā Second, I draw on theories of substantive equality from law and philosophy to propose an alternative methodology, which I call āsubstantive 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 āfairā decision-making and toward substantive evaluations of whether andĀ how algorithms can promote justiceĀ in practice.