The prevalence and importance of algorithmic two-sided marketplaces has drawn attention to the issue of fairness in such settings. Algorithmic decisions are used in assigning students to schools, users to advertisers, and applicants to job interviews. These decisions should heed the preferences of individuals, and simultaneously be fair with respect to their merits (synonymous with fit, future performance, or need). Merits conditioned on observable features are always \emph{uncertain}, a fact that is exacerbated by the widespread use of machine learning algorithms to infer merit from the observables. As our key contribution, we carefully axiomatize a notion of individual fairness in the two-sided marketplace setting which respects the uncertainty in the merits; indeed, it simultaneously recognizes uncertainty as the primary potential cause of unfairness and an approach to address it. We design a linear programming framework to find fair utility-maximizing distributions over allocations, and we show that the linear program is robust to perturbations in the estimated parameters of the uncertain merit distributions, a key property in combining the approach with machine learning techniques.

翻译：算法化双边市场平台的普遍性与重要性使其中的公平性问题备受关注。算法决策被用于将学生分配至学校、用户匹配广告商、求职者安排面试。这些决策既需尊重个体偏好，同时应基于其素质（与适配度、未来表现或需求同义）保持公平。基于可观测特征的条件素质始终存在\emph{不确定性}，而机器学习算法广泛用于从可观测信息推断素质进一步加剧了这一问题。作为核心贡献，我们严谨地公理化构建了双边市场背景下兼顾素质不确定性的个体公平性概念——该概念既将不确定性视为不公平的主要潜在成因，也将其视为解决不公平的途径。我们设计了线性规划框架以寻求分配上的公平效用最大化分布，并证明该线性规划对不确定性素质分布估计参数的扰动具有鲁棒性，这是将该方法有效结合机器学习技术的关键性质。