Multi-party Computation Protocols for Post-Market Fairness Monitoring in Algorithmic Hiring: From Legal Requirements to Computational Designs

TL;DR

The paper addresses the challenge of post-market fairness monitoring for high-risk algorithmic hiring under the EU AI Act and GDPR, where access to sensitive attributes is restricted. It proposes a privacy-preserving, multi-party computation framework co-designed with legal and industry stakeholders to securely compute fairness metrics without exposing individual attributes. The authors derive end-to-end design requirements, develop a practical MPC-based protocol spanning data collection, encryption, computation, and visualization, and validate it in a large-scale industrial setting via an operational dashboard. The work offers actionable design guidelines and regulatory implications for deploying MPC-based post-market fairness monitoring in algorithmic hiring, highlighting governance, usability, and data protection considerations.

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.

Figures (3)

• Figure 1: Design features of the MPC protocol for fairness monitoring based on design requirements.
• Figure 2: The framework of the MPC protocol for fairness monitoring.
• Figure 3: Industrial fairness monitoring dashboard implementing the proposed privacy-preserving protocol.