The field of algorithmic fairness has rapidly emerged over the past 15 years as algorithms have become ubiquitous in everyday lives. Algorithmic fairness traditionally considers statistical notions of fairness algorithms might satisfy in decisions based on noisy data. We first show that these are theoretically disconnected from welfare-based notions of fairness. We then discuss two individual welfare-based notions of fairness, envy freeness and prejudice freeness, and establish conditions under which they are equivalent to error rate balance and predictive parity, respectively. We discuss the implications of these findings in light of the recently discovered impossibility theorem in algorithmic fairness (Kleinberg, Mullainathan, & Raghavan (2016), Chouldechova (2017)).

翻译：算法公平性领域在过去15年间迅速发展，因为算法已变得无处不在于日常生活中。传统上，算法公平性关注基于噪声数据的决策中算法可能满足的统计公平性概念。我们首先指出，这些概念与基于福利的公平性概念在理论上没有关联。接着，我们讨论两种基于个体福利的公平性概念——无嫉妒性和无偏见性，并建立它们分别与错误率平衡和预测奇偶性等价的条件。最后，我们结合算法公平性中近期发现的不可能性定理（Kleinberg, Mullainathan, & Raghavan (2016), Chouldechova (2017)），探讨这些发现的启示。