Recent years have seen rapid growth in the market for HR technology and AI-driven HR solutions in particular. This popularity has also resulted in increased attention to the negative aspects of using AI to support hiring practices, such as the risk of reinforcing existing biases against vulnerable groups based on gender or other sensitive attributes. Combining human experience with AI efficiency in making recruiting and selection decisions has the potential to help mitigate these biases, but despite a considerable amount of research on fairness in algorithmic hiring, actual empirical evaluations comparing the fairness of human, AI, and human-augmented decision-making remain scarce. In this study, we address this gap by presenting a quantitative analysis of gender bias across three scenarios of a real-world recruitment platform: (1) recruiters searching a CV database manually for relevant candidates, (2) AI-driven matching between candidates and jobs, and (3) a combination of human and AI-driven recruiting. We find that human recruiters produce lists of candidates that are fairer in terms of gender than the AI-only solution, with more deliberation by humans resulting in fairer outcomes. However, the combination of human and AI-driven is more than the sum of its parts and produces the fairest candidate lists: interacting with the slate of recommended candidates first before manually searching for additional candidates has a beneficial effect on the gender fairness of the set of candidates that are viewed, clicked, and contacted afterwards. Our work provides one of the first empirical comparisons of fairness across human, AI, and hybrid recruiting processes, offering evidence to inform the development of more equitable hiring practices and highlighting the importance of human oversight for mitigating bias in algorithmic hiring.

翻译：近年来，人力资源技术市场，特别是人工智能驱动的人力资源解决方案，呈现快速增长态势。这种普及性也使得人们日益关注使用人工智能辅助招聘实践所带来的负面影响，例如基于性别或其他敏感属性强化对弱势群体现有偏见的风险。将人类经验与人工智能效率相结合以制定招聘与选拔决策，有望帮助缓解这些偏见；然而，尽管已有大量关于算法招聘公平性的研究，但实际比较人类、人工智能以及人机协同决策公平性的实证评估仍然匮乏。本研究通过定量分析现实招聘平台三种场景下的性别偏见，以填补这一空白：（1）招聘人员手动搜索简历数据库以寻找相关候选人；（2）人工智能驱动的候选人与职位匹配；（3）人类与人工智能协同的招聘模式。我们发现，在性别公平性方面，人类招聘人员生成的候选人名单比纯人工智能方案更为公平，且人类更审慎的决策过程会带来更公平的结果。然而，人机协同模式的效果超越了其各部分的简单叠加，能够产生最公平的候选人名单：在手动搜索额外候选人之前，首先与系统推荐的候选人列表进行交互，会对后续被查看、点击及联系的候选人集合的性别公平性产生积极影响。我们的工作首次对人工、纯算法及混合招聘流程的公平性进行了实证比较，为开发更公平的招聘实践提供了证据，并凸显了人类监督对于缓解算法招聘偏见的重要性。