公平性监管对机构政策与人口资质的影响
Impact of Fairness Regulations on Institutions' Policies and Population Qualifications
AI总结：
该研究探讨公平性监管中歧视惩罚对选拔差异的作用，分析个体资质随政策演变时惩罚可能阻碍公平自然实现的场景，并提出抵消不良结果、保障公平的条件。
摘要：
算法系统的普及引发了关于其社会影响的监管与管控的讨论。本文研究了一个以选拔最具资质的个体、最大化效用为核心目标的系统。为在选拔算法中推动人口统计 parity（人口均等），我们考虑对不同社会群体间的歧视施加惩罚。我们探究了歧视惩罚能有效降低选拔差异的条件。此外，我们还探讨了当个体资质会随时间因施加的惩罚政策而变化时，此类惩罚的影响。我们识别出了惩罚可能阻碍人口内部公平自然实现的场景。另外，我们提出了若干能够抵消这一不良结果、从而保障公平性的条件。
英文摘要：
The proliferation of algorithmic systems has fueled discussions surrounding the regulation and control of their social impact. Herein, we consider a system whose primary objective is to maximize utility by selecting the most qualified individuals. To promote demographic parity in the selection algorithm, we consider penalizing discrimination across social groups. We examine conditions under which a discrimination penalty can effectively reduce disparity in the selection. Additionally, we explore the implications of such a penalty when individual qualifications may evolve over time in response to the imposed penalizing policy. We identify scenarios where the penalty could hinder the natural attainment of equity within the population. Moreover, we propose certain conditions that can counteract this undesirable outcome, thus ensuring fairness.
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。
