同一家算法供应商导致求职者被集体拒，种族差异明显。
Algorithmic Monocultures in Hiring
- 用同一算法批量筛选300万申请，发现种族差异显著。
- 亚裔和非裔申请被不利影响比例分别为14.74%和25.87%。
- 多数人只被拒一次，但4%的人全被拒，说明算法趋同风险高。
许多雇主使用少数几家算法供应商的系统筛选求职者。我们推测，算法垄断会导致相同个体及同种族群体遭遇一致拒录。我们获取并分析了一个全新数据集，包含300万申请人提交的400万份申请，所有申请均由同一供应商的算法筛选。结果显示明显的种族差异：在亚裔和非裔申请人的全部申请中，分别有14.74%和25.87%的申请落入对这两类人群产生不利影响的职位（依据美国就业歧视标准）。个体结果高度同质化——4%的申请人申请10个职位后，均被推荐拒录，该比例远高于随机预期。为理解这种同质性，我们利用招聘算法的确定性可复现特性，模拟申请人若投递所有职位可能获得的结果。结果显示，只有广泛投递，才能确保申请被人类审核。
原文摘要 · Abstract (English)
Many employers screen job applicants with algorithms built by the same few algorithm vendors. We hypothesize that algorithmic monoculture leads to the same individuals and members of the same racial groups facing rejection. We acquire and analyze a novel dataset of 3 million applicants submitting 4 million applications where all the applications are screened by algorithms built by the same vendor. We find clear racial disparities in applicant outcomes. Of all applications submitted by Asian and Black applicants, 14.74% and 25.87% are submitted to positions that adversely impact Asian and Black applicants, respectively, according to U.S. employment discrimination standards. Individuals also receive homogeneous outcomes: 4% of all applicants who apply to 10 positions are recommended for rejection from all positions, a rate higher than expected by chance. To better understand this homogeneity, we leverage the deterministic replicability of hiring algorithms to generate the outcomes applicants would have received if they applied to all positions. We show that applicants would need to apply widely in order to ensure their applications are considered by a human
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