The Reasonable Person Standard for AI

TL;DR

The paper argues that the reasonable person standard, a cornerstone of American law, should guide AI governance by defining the expected, intelligible, and foreseeable behaviors AI systems ought to exhibit. It surveys how reasonableness manifests across contract, tort, and criminal law, using canonical cases to illustrate implications for AI interpretation, implied terms, foreseeability, and intent. It then discusses applying these standards to AI design, evaluation, and safety, while highlighting limitations, cultural differences, and the need for explainability. Finally, it calls for integrating cognitive and social science insights to establish qualitative, context-sensitive benchmarks that can evolve with society’s understanding of reasonable behavior.

Abstract

As AI systems are increasingly incorporated into domains where human behavior has set the norm, a challenge for AI governance and AI alignment research is to regulate their behavior in a way that is useful and constructive for society. One way to answer this question is to ask: how do we govern the human behavior that the models are emulating? To evaluate human behavior, the American legal system often uses the "Reasonable Person Standard." The idea of "reasonable" behavior comes up in nearly every area of law. The legal system often judges the actions of parties with respect to what a reasonable person would have done under similar circumstances. This paper argues that the reasonable person standard provides useful guidelines for the type of behavior we should develop, probe, and stress-test in models. It explains how reasonableness is defined and used in key areas of the law using illustrative cases, how the reasonable person standard could apply to AI behavior in each of these areas and contexts, and how our societal understanding of "reasonable" behavior provides useful technical goals for AI researchers.