论文标题
不要让Ricci诉Destefano拒绝您：雇用悖论的偏见意识法律解决方案
Don't let Ricci v. DeStefano Hold You Back: A Bias-Aware Legal Solution to the Hiring Paradox
论文作者
论文摘要
试图解决就业不平等的公司面临招聘悖论。未能解决劳动力失衡可能会导致法律制裁和审查，但积极的解决这些问题的措施可能会导致同样的法律冲突。 Microsoft和Wells Fargo最近与劳工部联邦合同合规计划（OFCCP）的奔跑不隔离，并且很可能会持续存在。除了混乱外，Ricci诉Destefano的现有奖学金经常认为解决了这种悖论的解决方案。规避诸如4/5的规则之类的实践进一步说明了动作太少与行动太多之间的紧张关系。 在这项工作中，我们提供了一种有力的方法来解决这种追踪法律和算法挑战的招聘悖论。我们解开了Ricci诉Destefano的细微差别，并扩展了法律文献，认为某些算法方法可以通过引入乐队来评估候选人的法律实践来允许使用。因此，我们表明，偏见感知技术可用于诊断和减轻就业管道中的“内置”逆风。我们使用部分有序集的机械来处理评估数据中不确定性的存在。这种方法使我们能够从对待“人数”转移到将人视为个人的情况下，这是在就业背景下由第七章寻求的财产。
Companies that try to address inequality in employment face a hiring paradox. Failing to address workforce imbalance can result in legal sanctions and scrutiny, but proactive measures to address these issues might result in the same legal conflict. Recent run-ins of Microsoft and Wells Fargo with the Labor Department's Office of Federal Contract Compliance Programs (OFCCP) are not isolated and are likely to persist. To add to the confusion, existing scholarship on Ricci v. DeStefano often deems solutions to this paradox impossible. Circumventive practices such as the 4/5ths rule further illustrate tensions between too little action and too much action. In this work, we give a powerful way to solve this hiring paradox that tracks both legal and algorithmic challenges. We unpack the nuances of Ricci v. DeStefano and extend the legal literature arguing that certain algorithmic approaches to employment are allowed by introducing the legal practice of banding to evaluate candidates. We thus show that a bias-aware technique can be used to diagnose and mitigate "built-in" headwinds in the employment pipeline. We use the machinery of partially ordered sets to handle the presence of uncertainty in evaluations data. This approach allows us to move away from treating "people as numbers" to treating people as individuals -- a property that is sought after by Title VII in the context of employment.
