普通人评估AI公平性比专家更复杂，且要求更严。
"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment
- 让无技术背景的26人模拟信用评估决策，观察其公平性判断。
- 他们关注非法律保护特征，自定义指标，设定更严格阈值。
- 适合参与AI治理设计的政策制定者与公众代表参考。
评估人工智能（AI）公平性通常由技术专家主导，包括选择受保护特征、公平性度量和设置公平阈值。然而，对受影响但缺乏AI知识的利益相关者如何评估公平性了解甚少。为此，我们对26名无AI专业知识的利益相关者进行了质性研究，他们在信贷评级场景中扮演决策者角色，评估特征优先级、度量方法及阈值设定。结果发现，利益相关者的公平性决策远比典型专家实践复杂：他们不仅考虑法律未保护的特征，还根据具体情境定制度量方式，设定多样但更严格的公平阈值，并倾向于设计个性化公平方案。研究揭示了利益相关者在AI公平性治理与缓解中的实质性贡献潜力，强调纳入其细致入微的公平判断的重要性。
原文摘要 · Abstract (English)
Assessing fairness in artificial intelligence (AI) typically involves AI experts who select protected features, fairness metrics, and set fairness thresholds to assess outcome fairness. However, little is known about how stakeholders, particularly those affected by AI outcomes but lacking AI expertise, assess fairness. To address this gap, we conducted a qualitative study with 26 stakeholders without AI expertise, representing potential decision subjects in a credit rating scenario, to examine how they assess fairness when placed in the role of deciding on features with priority, metrics, and thresholds. We reveal that stakeholders' fairness decisions are more complex than typical AI expert practices: they considered features far beyond legally protected features, tailored metrics for specific contexts, set diverse yet stricter fairness thresholds, and even preferred designing customized fairness. Our results extend the understanding of how stakeholders can meaningfully contribute to AI fairness governance and mitigation, underscoring the importance of incorporating stakeholders' nuanced fairness judgments.
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