When Feasibility of Fairness Audits Relies on Willingness to Share Data: Examining User Acceptance of Multi-Party Computation Protocols for Fairness Monitoring

TL;DR

This study investigates how user acceptance of privacy-preserving multi-party computation protocols for fairness monitoring depends on protocol design and user perceptions, highlighting the importance of aligning privacy and fairness attributes.

Contribution

It provides empirical insights into user preferences and acceptance factors for MPC protocols in fairness monitoring, informing better deployment and communication strategies.

Findings

Users prioritize privacy mechanisms in direct evaluations.

Benefit attributes influence acceptance in simulated choices.

Acceptance depends on users' fairness and privacy orientations.

Abstract

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.