Post-market fairness monitoring is now mandated to ensure fairness and accountability for high-risk employment AI systems under emerging regulations such as the EU AI Act. However, effective fairness monitoring often requires access to sensitive personal data, which is subject to strict legal protections under data protection law. Multi-party computation (MPC) offers a promising technical foundation for compliant post-market fairness monitoring, enabling the secure computation of fairness metrics without revealing sensitive attributes. Despite growing technical interest, the operationalization of MPC-based fairness monitoring in real-world hiring contexts under concrete legal, industrial, and usability constraints remains unknown. This work addresses this gap through a co-design approach integrating technical, legal, and industrial expertise. We identify practical design requirements for MPC-based fairness monitoring, develop an end-to-end, legally compliant protocol spanning the full data lifecycle, and empirically validate it in a large-scale industrial setting. Our findings provide actionable design insights as well as legal and industrial implications for deploying MPC-based post-market fairness monitoring in algorithmic hiring systems.

翻译：根据欧盟《人工智能法案》等新兴法规的要求，高风险就业人工智能系统必须进行事后公平性监测，以确保其公平性与可问责性。然而，有效的公平性监测通常需要访问受数据保护法严格保护的个人敏感数据。多方计算（MPC）为合规的事后公平性监测提供了可行的技术基础，能够在无需暴露敏感属性的情况下安全计算公平性指标。尽管技术关注度日益增长，但在现实招聘场景中，基于MPC的公平性监测在具体法律、行业及可用性约束下的实际运作模式仍不明确。本研究通过融合技术、法律与行业专业知识的协同设计方法填补这一空白。我们明确了基于MPC的公平性监测的实际设计要求，开发了覆盖完整数据生命周期的端到端合规协议，并在大规模工业场景中进行了实证验证。研究结果为在算法招聘系统中部署基于MPC的事后公平性监测提供了可行的设计思路，以及法律与行业层面的实施启示。