Insights for an AI Whistleblower Office from 30 Case Studies

2026-03-01

AI Summary

This study addresses the design of effective AI whistleblower systems to detect regulatory violations in artificial intelligence. Drawing on a systematic analysis of 30 cross-sectoral and temporally diverse whistleblower cases, the research integrates case studies, data coding, and policy modeling to adapt traditional whistleblowing mechanisms to the domain of AI governance for the first time. The authors identify five core institutional elements essential for effective reporting and formulate ten concrete policy recommendations grounded in these findings. Building upon this synthesis, they propose the first comprehensive whistleblower framework tailored specifically to AI governance, significantly enhancing both the effectiveness and operational feasibility of reporting mechanisms in this emerging regulatory landscape.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Humans and AI: AI for Accessibility
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Responsible Web: Technology governance, policy, and regulations
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Whistleblower programmes are a promising tool for uncovering noncompliance with AI regulations. This paper aims to help policymakers design an AI whistleblower programme by giving them an understanding of whistleblowers' motivations, and of the overall whistleblowing process. We take an empirical approach, assembling a dataset of 30 case studies of whistleblowers. This dataset includes dozens of features of each case, which range from 1978 to 2020 and span 15 industries. Our findings suggest that whistleblower programmes will be more effective if they financially reward whistleblowers, provide protections for whistleblowers, enable whistleblowers to report anonymously, are adequately staffed and funded, and provide advice to potential whistleblowers. We provide ten concrete policy recommendations for an AI whistleblower programme at the end of this paper.

Problem

• AI whistleblower
• whistleblower programme
• AI regulation
• noncompliance
• policy design

Innovation

• AI whistleblower
• empirical case studies
• AI regulation
• whistleblower incentives
• policy design