The burgeoning field of "algorithmic fairness" provides a novel set of methods for reasoning about the fairness of algorithmic predictions and decisions. Yet even as algorithmic fairness has become a prominent component of efforts to enhance equality in domains such public policy, it also faces significant limitations and critiques. The most fundamental issue is the mathematical result known as the "impossibility of fairness" (an incompatibility between mathematical definitions of fairness). Furthermore, many algorithms that satisfy standards of fairness actually exacerbate oppression. These two issues call into question whether algorithmic fairness can play a productive role in the pursuit of equality. In this paper, I diagnose these issues as the product of algorithmic fairness methodology and propose an alternative path forward for the field. The dominant approach of "formal algorithmic fairness" suffers from a fundamental limitation: it relies on a narrow frame of analysis that is limited to specific decision-making processes, in isolation from the context of those decisions. In light of this shortcoming, I draw on theories of substantive equality from law and philosophy to propose an alternative method: "substantive algorithmic fairness." Substantive algorithmic fairness takes a more expansive scope to analyzing fairness, looking beyond specific decision points to account for social hierarchies and the impacts of decisions facilitated by algorithms. As a result, substantive algorithmic fairness suggests reforms that combat oppression and that provide an escape from the impossibility of fairness. Moreover, substantive algorithmic fairness presents a new direction for the field of algorithmic fairness: away from formal mathematical models of "fairness" and towards substantive evaluations of how algorithms can (and cannot) promote equality.
翻译:快速增长的“ 算法公平” 领域提供了一套关于算法预测和决定公平性推理的新型方法。 然而,即使算法公平性已成为加强此类公共政策领域平等的努力中一个突出的组成部分,它也面临着巨大的限制和批评。 最根本的问题是所谓的“ 公平可能性” ( 公平性数学定义不相容) 的数学结果。 此外,许多符合公平标准的算法实际上加剧了压迫。 这两个问题令人质疑算法公平性能否在追求平等方面发挥建设性作用。 在本文中,我将这些问题诊断为算法公平方法的产物,并提出外地的替代途径。 “ 正规算法公平性”的主导方法有根本性的局限性:它依赖于一个狭义的分析框架,它局限于特定的决策过程,而脱离这些决策的范围。 鉴于这一缺陷,我从法律和哲学的实质性平等性理论出发,可以提出一种替代方法: “ 实质性公平性算法公平性公平性” 实质性公平性模型从一个更深入的模型到一个分析公平性,它意味着一个更有利于战斗性的决定性的决定性的分析范围,“ ” 通过一种特定的算法的推理算,,它能够提供一种推算出一个更深的公平性的逻辑的高度的推算结果。