We investigate the level of success a firm achieves depending on which of two common scoring algorithms is used to screen qualified applicants belonging to a disadvantaged group. Both algorithms are trained on data generated by a prejudiced decision-maker independently of the firm. One algorithm favors disadvantaged individuals, while the other algorithm exemplifies prejudice in the training data. We deliver sharp guarantees for when the firm finds more success with one algorithm over the other, depending on the prejudice level of the decision-maker.

翻译：我们研究了企业根据两种常用评分算法筛选弱势群体合格申请者时所取得的成功程度。两种算法均基于与公司无关的有偏决策者生成的数据进行训练。一种算法偏向于弱势个体，而另一种算法则体现了训练数据中的歧视性。我们给出了清晰的理论保证，阐明在何种情况下企业使用一种算法会比另一种算法获得更高成功，这取决于决策者的歧视程度。