关于甄选最佳候选人的跨部门平权行动政策
Intersectional Affirmative Action Policies for Top-k Candidates Selection
摘要：
我们研究的问题，从一个申请人，每个候选人都与一个分数，表明他/她的能力池中选择前k名的候选人。根据具体情况，如求职或大学录取，这些分数可能是标准化测试的结果或未来表现和效用的其他预测因素。我们考虑的情况是，某些候选人群体经历历史和现实的不利因素，使他们被接受的机会比其他群体低得多。在这种情况下，我们希望采取平权行动政策，以减少录取率的差距，同时避免最终被选中的候选人的能力大幅下降。我们的算法设计的动机是经常观察到的现象，即歧视不成比例地影响同时属于多个弱势群体的个人，这些弱势群体是按照性别，种族，性取向，社会经济地位和残疾等交叉维度定义的。简而言之，我们的算法的目标是同时：选择具有高效用的候选人，并提高弱势交叉类的代表性。这自然涉及权衡，并且由于考虑更多属性时潜在子组的组合爆炸而在计算上具有挑战性。我们提出了两种算法来解决这个问题，分析它们，并使用OECD国家的大学申请分数和学士学位录取数据集进行实验评估。我们的结论是，这是可能的，以显着减少影响交叉类的录取率的差距，在选定的候选人的能力方面有一个小的损失。据我们所知，我们是第一个研究公平约束方面的交叉类的背景下，前k选择。
英文摘要：
We study the problem of selecting the top-k candidates from a pool of applicants, where each candidate is associated with a score indicating his/her aptitude. Depending on the specific scenario, such as job search or college admissions, these scores may be the results of standardized tests or other predictors of future performance and utility. We consider a situation in which some groups of candidates experience historical and present disadvantage that makes their chances of being accepted much lower than other groups. In these circumstances, we wish to apply an affirmative action policy to reduce acceptance rate disparities, while avoiding any large decrease in the aptitude of the candidates that are eventually selected. Our algorithmic design is motivated by the frequently observed phenomenon that discrimination disproportionately affects individuals who simultaneously belong to multiple disadvantaged groups, defined along intersecting dimensions such as gender, race, sexual orientation, socio-economic status, and disability. In short, our algorithm's objective is to simultaneously: select candidates with high utility, and level up the representation of disadvantaged intersectional classes. This naturally involves trade-offs and is computationally challenging due to the the combinatorial explosion of potential subgroups as more attributes are considered. We propose two algorithms to solve this problem, analyze them, and evaluate them experimentally using a dataset of university application scores and admissions to bachelor degrees in an OECD country. Our conclusion is that it is possible to significantly reduce disparities in admission rates affecting intersectional classes with a small loss in terms of selected candidate aptitude. To the best of our knowledge, we are the first to study fairness constraints with regards to intersectional classes in the context of top-k selection.
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。
