存在偏差时带偏好的集中选择
Centralized Selection with Preferences in the Presence of Biases
AI总结：
针对多机构容量受限且候选人带偏好、部分群体观测效用存在系统性偏差的集中选择问题，本文提出一种在分布假设下兼顾近最优偏好公平与近最大真实效用的算法，并通过真实与合成实验验证。
摘要：
本文考虑这样一种情形：存在多个机构，每个机构对候选人的容量有限；同时存在候选人，每位候选人对各机构都有偏好。一个中央实体评估每位候选人对各机构的效用，目标是以最大化效用的方式为每个机构选择候选人，同时兼顾候选人的偏好。本文聚焦于候选人被划分为多个群体、且某些群体中候选人的观测效用存在偏差——系统性地低于其真实效用——的设定。第一个结果表明，在这些存在偏差的设定中，现有算法可能导致所选结果的真实效用次优，并使各群体中获得其偏好选择的候选人比例出现显著差异。随后，本文提出一种算法，并证明在分布假设下，该算法产生的选择在偏好方面实现近乎最优的群体公平性，同时近乎最大化真实效用。此外，本文还在真实和合成设定中对这些结果进行了广泛的实证验证，而在这些设定中分布假设可能并不成立。
英文摘要：
This paper considers the scenario in which there are multiple institutions, each with a limited capacity for candidates, and candidates, each with preferences over the institutions. A central entity evaluates the utility of each candidate to the institutions, and the goal is to select candidates for each institution in a way that maximizes utility while also considering the candidates' preferences. The paper focuses on the setting in which candidates are divided into multiple groups and the observed utilities of candidates in some groups are biased--systematically lower than their true utilities. The first result is that, in these biased settings, prior algorithms can lead to selections with sub-optimal true utility and significant discrepancies in the fraction of candidates from each group that get their preferred choices. Subsequently, an algorithm is presented along with proof that it produces selections that achieve near-optimal group fairness with respect to preferences while also nearly maximizing the true utility under distributional assumptions. Further, extensive empirical validation of these results in real-world and synthetic settings, in which the distributional assumptions may not hold, are presented.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
