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

2026-02-02

AI Summary

Fairness auditing typically requires users to share sensitive data, yet user acceptance of multiparty computation (MPC) protocols designed to enable such auditing under privacy-compliant conditions remains unclear. This study addresses this gap through an online survey of 833 European participants, combining a discrete choice experiment with direct evaluation questionnaires to systematically examine user acceptance of different MPC protocol designs for fairness monitoring. For the first time, it reveals a divergence in how users weigh risk-related attributes (e.g., privacy protection) against benefit-related attributes (e.g., fairness objectives), and integrates individual privacy and fairness preferences into an acceptance model. Results show that while users prioritize privacy mechanisms in direct evaluations, they place greater emphasis on fairness outcomes in simulated choices—both factors significantly shaping willingness to adopt MPC protocols and offering critical behavioral insights for compliant deployment.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Humans and AI: Learning Human Values and Preferences

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Security and Privacy: Large-scale security measurements

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.

Problem

• fairness auditing
• user acceptance
• multi-party computation
• privacy-preserving protocols
• algorithmic bias

Innovation

• multi-party computation
• fairness auditing
• user acceptance
• privacy-preserving protocols
• algorithmic bias