论文标题
算法公平和统计歧视
Algorithmic Fairness and Statistical Discrimination
论文作者
论文摘要
算法公平是一个新的跨学科研究领域，重点介绍了如何衡量过程或算法是否可能无意中产生不公平的结果，以及是否可以减轻此类过程的潜在不公平性。统计歧视描述了一系列信息问题，即使没有歧视意图，也会引起理性（即贝叶斯）决策，从而导致不公平的结果。在本文中，我们提供了这两个相关文献的概述，并在它们之间建立了联系。比较既说明了理性与公平性之间的冲突以及内生性的重要性（例如，“理性期望”和“自我实现的预言”）在定义和追求公平性方面的冲突。我们认为，这两种传统提出了一种考虑新的公平概念的价值，这些概念明确说明了算法打算如何衡量的个体特征可能会因算法而变化。
Algorithmic fairness is a new interdisciplinary field of study focused on how to measure whether a process, or algorithm, may unintentionally produce unfair outcomes, as well as whether or how the potential unfairness of such processes can be mitigated. Statistical discrimination describes a set of informational issues that can induce rational (i.e., Bayesian) decision-making to lead to unfair outcomes even in the absence of discriminatory intent. In this article, we provide overviews of these two related literatures and draw connections between them. The comparison illustrates both the conflict between rationality and fairness and the importance of endogeneity (e.g., "rational expectations" and "self-fulfilling prophecies") in defining and pursuing fairness. Taken in concert, we argue that the two traditions suggest a value for considering new fairness notions that explicitly account for how the individual characteristics an algorithm intends to measure may change in response to the algorithm.
