公平性审计的可行性依赖于数据共享的意愿：考察多方计算协议在公平性监控中的用户接受度
When Feasibility of Fairness Audits Relies on Willingness to Share Data: Examining User Acceptance of Multi-Party Computation Protocols for Fairness Monitoring
AI总结：
研究探讨了用户对多方计算协议在公平性监控中的接受度，发现用户对隐私保护和公平性目标的重视影响其数据共享意愿。
摘要：
公平性监控对于检测算法偏见至关重要，这已成为欧盟人工智能法案所要求的。由于此类监控需要敏感用户数据（例如种族），人工智能法案只允许在严格隐私措施下处理这些数据，例如多方计算（MPC），以符合GDPR的规定。然而，此类安全监控协议的有效性最终取决于人们的数据共享意愿。目前对不同MPC协议设计如何影响用户接受度知之甚少。为此，我们进行了一个针对欧洲833名参与者的在线调查，研究不同MPC协议设计在公平性监控中的用户接受度。研究发现，用户在直接评估时优先考虑与风险相关的属性（例如隐私保护机制），但在模拟选择中则优先考虑与收益相关的属性（例如公平性目标），用户的接受度受到其公平性和隐私取向的影响。我们得出结论，部署和沟通隐私保护协议应以促进知情同意并符合用户期望的方式进行。
英文摘要：
Fairness monitoring is critical for detecting algorithmic bias, as mandated by the EU AI Act. Since such monitoring requires sensitive user data (e.g., ethnicity), the AI Act permits its processing only with strict privacy measures, such as multi-party computation (MPC), in compliance with the GDPR. However, the effectiveness of such secure monitoring protocols ultimately depends on people's willingness to share their data. Little is known about how different MPC protocol designs shape user acceptance. To address this, we conducted an online survey with 833 participants in Europe, examining user acceptance of various MPC protocol designs for fairness monitoring. Findings suggest that users prioritized risk-related attributes (e.g., privacy protection mechanism) in direct evaluation but benefit-related attributes (e.g., fairness objective) in simulated choices, with acceptance shaped by their fairness and privacy orientations. We derive implications for deploying and communicating privacy-preserving protocols in ways that foster informed consent and align with user expectations.
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。
