美国超过 90% 的雇主依赖招聘算法来筛选求职者。许多不同的雇主都使用来自少数几家供应商的算法。我们开展了迄今为止规模最大的算法招聘实证研究，数据涵盖 340 万名真实求职者，他们向横跨 11 个市场领域的 156 家雇主提交了 400 万份申请。每一份申请都由同一家供应商的算法进行评估：我们测试了这种算法单一文化是否对就业机会造成了瓶颈。我们是首个在大规模数据中证明，高风险的招聘决策存在种族差异和同质化结果的研究。
关键发现
1
对亚裔和黑人群体的广泛不利影响。
我们是首个在已部署的算法招聘中证明不利影响的研究，这也是在真实高风险 AI 决策中展示不公平结果的最大规模实证之一。
25.87% 的申请
由黑人求职者提交，
14.74%
由亚裔求职者提交的申请，被导向了根据相关
美国就业法（第七章）
标准对其产生不利影响的职位。
2
不利影响仅通过按职位逐一分解分析才得以揭示。
尽管由于数据获取限制，算法招聘的实证研究非常受限，但此前的研究
显示
在将供应商所有数据作为一个整体研究时，不利影响极小。通过按照第七章的标准分别研究每个职位，我们识别出了那些在整体数据中被掩盖的不利影响职位。
3
招聘中的算法单一文化导致系统性拒绝。
我们是首个在已部署的算法招聘中证明系统性拒绝的研究，正如此前
理论
所提出的那样。
Over 90% of U.S. employers rely on hiring algorithms to screen job applicants. Many different employers use algorithms from the same few vendors. We conduct the largest empirical study of algorithmic hiring with data for 3.4 million real job applicants submitting 4 million applications to 156 employers across 11 market sectors. Every application was assessed by algorithms from a single vendor: we test whether this algorithmic monoculture bottlenecks job opportunities. We are the first to demonstrate large-scale evidence of racial disparities and homogeneous outcomes in high-stakes hiring decisions.
Key Findings
1
Large-scale adverse impact for Asians and Blacks.
We are the first to demonstrate adverse impact in deployed algorithmic hiring as one of the largest demonstrations of unfair outcomes in real high-stakes AI decisions.
25.87% of applications
submitted by Black applicants and
14.74%
of applications submitted by Asian applicants are directed to positions that adversely impact them based on the standards of the relevant
U.S. employment law (Title VII)
.
2
Adverse impact only revealed by disaggregated position-by-position analysis.
While empirical studies of algorithmic hiring are very constrained due to data access limitations, prior studies
showed
minimal adverse impact due studying all of the vendor's data as a whole. By studying each position separately, in accordance with the standards of Title VII, we identify positions that demonstrate adverse impact that gets washed out in aggregate.
3
Algorithmic monocultures in hiring yield systemic rejections.
We are the first to demonstrate systemic rejections in deployed algorithmic hiring as posited in
prior
theoretical