The Reasonable Person Standard for AI

TL;DR

This paper explores how the legal concept of the 'Reasonable Person Standard' can guide the development and evaluation of AI systems to ensure their behavior aligns with societal norms and expectations.

Contribution

It introduces the application of the legal 'Reasonable Person Standard' as a framework for defining, assessing, and stress-testing AI behavior in societal contexts.

Findings

Legal standards can inform AI behavior guidelines

Reasonableness criteria can be operationalized for AI evaluation

Societal norms of reasonableness provide technical goals for AI development

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.