Insights for an AI Whistleblower Office from 30 Case Studies

TL;DR

This paper analyzes 30 case studies to provide insights and policy recommendations for designing effective AI whistleblower programmes that encourage reporting and protect whistleblowers.

Contribution

It offers an empirical analysis of whistleblower motivations and processes, leading to ten practical policy recommendations for AI whistleblower systems.

Findings

Financial rewards increase reporting likelihood

Protection and anonymity encourage whistleblowing

Adequate staffing and funding improve programme effectiveness

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.