Co-designing for Compliance: Multi-party Computation Protocols for Post-Market Fairness Monitoring in Algorithmic Hiring

TL;DR

This paper develops and empirically validates a legally compliant multi-party computation protocol for fairness monitoring in algorithmic hiring, addressing legal, industrial, and usability challenges.

Contribution

It presents a novel co-designed MPC protocol tailored for real-world, legally compliant fairness monitoring in employment AI systems.

Findings

Developed an end-to-end MPC protocol for fairness monitoring

Validated the protocol in a large-scale industrial setting

Provided actionable insights for deploying MPC in hiring systems

Abstract

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.