用于检测辨别力的快速阈值测试
Fast Threshold Tests for Detecting Discrimination
摘要：
门槛测试最近被提出作为一种有用的方法，用于检测贷款，雇用和警务决策中的偏见。例如，在信贷扩展方面，这些测试旨在估计向白人和少数群体申请人发放贷款的门槛，对少数群体的推断门槛较高，表明存在歧视。然而，这种技术需要拟合复杂的贝叶斯潜变量模型，其推理通常在计算上具有挑战性。在这里，我们开发了一种用于拟合阈值测试的方法，该方法比现有方法快两个数量级，将计算从几小时减少到几分钟。为了实现这些性能增益，我们引入并分析了区间[0，1]上的一系列灵活的概率分布-我们称之为判别分布-这是计算效率高的工作。我们通过分析纽约市270万次警察拦截行人来展示我们的技术。
英文摘要：
Threshold tests have recently been proposed as a useful method for detecting bias in lending, hiring, and policing decisions. For example, in the case of credit extensions, these tests aim to estimate the bar for granting loans to white and minority applicants, with a higher inferred threshold for minorities indicative of discrimination. This technique, however, requires fitting a complex Bayesian latent variable model for which inference is often computationally challenging. Here we develop a method for fitting threshold tests that is two orders of magnitude faster than the existing approach, reducing computation from hours to minutes. To achieve these performance gains, we introduce and analyze a flexible family of probability distributions on the interval [0, 1] -- which we call discriminant distributions -- that is computationally efficient to work with. We demonstrate our technique by analyzing 2.7 million police stops of pedestrians in New York City.
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。
