Position: The Reasonable Person Standard for AI
Authors: Sunayana Rane
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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. |
| Researcher Affiliation | Academia | Sunayana Rane 1 2 1Department of Computer Science, Princeton University, Princeton, NJ, USA 2University of Chicago Law School, Chicago, IL, USA. |
| Pseudocode | No | The paper is a conceptual discussion and does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper describes a conceptual framework and does not present a methodology that would involve source code release. |
| Open Datasets | No | The paper does not use or describe a machine learning dataset in the context of model training. It refers to legal cases as 'illustrative cases' and analogies like 'training data' for human judges, but not as a dataset for public access in the ML sense. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not involve software implementation details or dependencies. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |