无思考仅AI：有偏见的LLM招聘推荐改变人类决策并限制人类自主性
No Thoughts Just AI: Biased LLM Hiring Recommendations Alter Human Decision Making and Limit Human Autonomy
AI总结：
本研究通过简历筛选实验发现，有偏见的AI招聘推荐会显著改变人类决策，使人们高达90%的时间偏向AI偏爱的群体，并限制人类自主性，即使人们认为AI推荐质量低也仍受影响。
摘要：
在本研究中，我们进行了一项简历筛选实验（N=528），参与者与模拟的、表现出基于种族的偏好（偏见）的AI模型协作，评估16个高地位和低地位职业的候选人。模拟的AI偏见近似于现实世界AI系统中种族偏见的真实和反事实估计。我们调查了人们对白人、黑人、西班牙裔和亚裔候选人（通过质量控制简历上的名字和亲和团体表示）的偏好，涉及1,526个场景，并使用内隐联想测验（IATs）测量了他们在种族与地位之间的无意识关联，这些测验能预测歧视性招聘决策，但尚未在人类-AI协作中进行过研究。在没有AI或与没有种族偏好的AI一起做决策时，人们以相同的比率选择所有候选人。然而，当与偏爱特定群体的AI互动时，人们也高达90%的时间偏爱这些候选人，表明存在显著的行为转变。如果人们在简历筛选前完成IAT，选择身份与常见种族-地位刻板印象不一致的候选人的可能性可以增加13%。最后，即使人们认为AI推荐质量低或不重要，他们的决策在某些情况下仍然容易受到AI偏见的影响。这项工作对AI-HITL场景中人的自主性、AI与工作、AI招聘系统的设计与评估，以及减轻协作决策任务中偏见的策略具有意义。特别是，组织和监管政策在实施这些系统、教育使用者以及确定哪些系统应受监督时，应承认AI-HITL决策的复杂性。
英文摘要：
In this study, we conduct a resume-screening experiment (N=528) where people collaborate with simulated AI models exhibiting race-based preferences (bias) to evaluate candidates for 16 high and low status occupations. Simulated AI bias approximates factual and counterfactual estimates of racial bias in real-world AI systems. We investigate people's preferences for White, Black, Hispanic, and Asian candidates (represented through names and affinity groups on quality-controlled resumes) across 1,526 scenarios and measure their unconscious associations between race and status using implicit association tests (IATs), which predict discriminatory hiring decisions but have not been investigated in human-AI collaboration. When making decisions without AI or with AI that exhibits no race-based preferences, people select all candidates at equal rates. However, when interacting with AI favoring a particular group, people also favor those candidates up to 90% of the time, indicating a significant behavioral shift. The likelihood of selecting candidates whose identities do not align with common race-status stereotypes can increase by 13% if people complete an IAT before conducting resume screening. Finally, even if people think AI recommendations are low quality or not important, their decisions are still vulnerable to AI bias under certain circumstances. This work has implications for people's autonomy in AI-HITL scenarios, AI and work, design and evaluation of AI hiring systems, and strategies for mitigating bias in collaborative decision-making tasks. In particular, organizational and regulatory policy should acknowledge the complex nature of AI-HITL decision making when implementing these systems, educating people who use them, and determining which are subject to oversight.
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。
