Efforts to promote equitable public policy with algorithms appear to be fundamentally constrained by the "impossibility of fairness" (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 "formal algorithmic fairness." Because formal algorithmic fairness restricts analysis to isolated decision-making procedures, it leads to the impossibility of fairness and to models that exacerbate oppression despite appearing "fair." Second, I draw on theories of substantive equality from law and philosophy to propose an alternative methodology, which I call "substantive algorithmic fairness." 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 "fair" decision-making and toward substantive evaluations of whether and how algorithms can promote justice in practice.
翻译:以算法促进公平公共政策的努力似乎受到“ 不公平” ( 数学公平定义不相容 ) ( 数学公平定义不相容 ) 的根本性限制。 这一技术限制提出了关于算法公平的一个中心问题: 计算机科学家和决策者如何能支持使用算法的公平政策改革? 在本条中,我争论说,用算法促进正义需要改革算法公平的方法。 首先,我分析了目前算法公平方法的问题,我称之为“ 正规算法公平 ” 。 由于正式算法公平限制了对孤立决策程序的分析,因此它导致不可能实现公平,并导致使压迫恶化的模型,尽管出现“公平 ” 。 其次,我引用法律和哲学中的实质性平等理论来提出一种替代方法,我称之为“ 实质性算法公平 ” 。 因为实质性算法公平需要更加广泛的分析范围,它能够摆脱不可能实现的公平性,并为减轻算法的不公正提供了严格的指南。 总之, 实质性算法公平为算法公平提供了一个新的方向: 远离“ 公平” 决策的正式数学模式, 和 实质性评估是否和如何在实践中促进正义。