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Citation

Di Campli San Vito P, Fringi E, Johnston P, Bezerra LCT, Aristodemou M, Shahandashti SF, O'Hara E, Fiona Whyte L, Luo L, Wong M, Soufan A, Moshfeghi Y & Stumpf S (2026) Empowering Stakeholders with Participatory Auditing of Predictive AI: Perspectives from End-Users and Decision Subjects without AI Expertise. In: CHI '26: Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. ACM Conference on Human Factors in Computing Systems, Barcelona, Spain, 13.04.2026-17.04.2026. https://doi.org/10.1145/3772318.3791757

Abstract
Artificial intelligence (AI) applications have become ubiquitous in their impact on individuals and society, highlighting a crucial need for their responsible development. Recent research has called for participatory AI auditing, empowering individuals without AI expertise to audit AI applications throughout the entire AI development pipeline. Our work focuses on investigating how to support these kinds of auditors through participatory AI auditing tools and processes. We conducted a series of co-design workshops, using two health-related predictive AI applications as examples. Our results show that participants wanted to be part of AI audits, and were insightful in identifying the potential impacts of applications, but needed to be assisted in conducting audits, especially how to measure impacts. Importantly, participants provided examples of impacts not considered in current risk/harm taxonomies. Our findings provide implications for the design of tools and processes to empower everyone to contribute to responsible AI development in the future.

Keywords
Predictive AI; Participatory Auditing; Co-Design; Responsible AI; Harms; Benefits; Health; End-Users; Decision Subjects

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