Summary of A Path Towards Legal Autonomy: An Interoperable and Explainable Approach to Extracting, Transforming, Loading and Computing Legal Information Using Large Language Models, Expert Systems and Bayesian Networks, by Axel Constant et al.
A Path Towards Legal Autonomy: An interoperable and explainable approach to extracting, transforming, loading and computing legal information using large language models, expert systems and Bayesian networks
by Axel Constant, Hannes Westermann, Bryan Wilson, Alex Kiefer, Ines Hipolito, Sylvain Pronovost, Steven Swanson, Mahault Albarracin, Maxwell J.D. Ramstead
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY); Logic in Computer Science (cs.LO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to achieving legal autonomy for artificial intelligence agents is presented in this paper. The authors propose two methods: imposing constraints on AI actors and resources or encoding extant rules into AI agent software. The latter method requires a system that can extract, load, transform, and compute legal information, making it explainable and legally interoperable. This paper sketches a proof of principle using large language models, legal decision paths, and Bayesian networks. The proposed method is demonstrated through an application to extant regulation in autonomous cars, such as the California Vehicle Code. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence agents can make decisions on their own, but how do we ensure they follow the law? This paper looks at two ways to achieve “legal autonomy” for AI agents: by controlling what they can do or by making them understand and follow existing rules. To do this, we need a way to extract and use legal information in a way that’s easy to understand and follows the law. The authors show how large language models, special legal systems, and statistical networks can work together to achieve this goal. They demonstrate their approach by applying it to rules about autonomous cars. |
