A Penalty Default Approach to Preemptive Harm Disclosure and Mitigation for AI Systems

Abstract

As AI industry matures, it is important to ensure that the organizations developing these systems have sufficient incentives to identify and mitigate risks and harm. Unfortunately, the profit motive is often misaligned with this goal. Successful work to identify or reduce risk rarely has direct tangible benefits. In this paper, we consider the use of regulatory penalty defaults as a way to counter these perverse incentives. A regulatory penalty default regime consists of two parts: A regulatory penalty default and a mechanism to bargain around the default. The regulatory penalty default induces private actors to research and mitigate potential harms in order to limit liability, making the benefits of risk mitigation tangible. The bargaining mechanism provides incentives for companies to go beyond achieving a prescriptive threshold of compliance in creating a compelling case for escape from the default. With a focus on the policy landscape in the United States, we propose and discuss potential regulatory penalty default regimes for AI systems. For each of our proposals, we also discuss accompanying regulatory pathways for the bargaining process. While regulatory penalty default regimes are not a panacea (we discuss several drawbacks of the proposed methods), they are an important tool to consider in the regulation of AI systems.