The Reasonable Person Standard for AI
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 41 references that are in the map. First posted on arXiv 7 Jun 2024.
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References · 8
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
- 2021Zero-Shot Text-to-Image Generationcited by 4,276
- 2022Survey of Hallucination in Natural Language Generationcited by 2,056
- 2016Concrete Problems in AI Safetycited by 1,582
- 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
- 2023Getting aligned on representational alignmentcited by 142
- 2024Concept Alignmentcited by 9
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