# 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

arXiv: https://arxiv.org/abs/2403.18537 · HTML: https://mlsanity.com/paper/2403.18537/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 55 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [Black-Box Access is Insufficient for Rigorous AI Audits](https://mlsanity.com/paper/2401.14446/) (2024)
- [The relationship between trust in AI and trustworthy machine learning technologies](https://mlsanity.com/paper/1912.00782/) (2019)
- [Can GPT-3 Perform Statutory Reasoning?](https://mlsanity.com/paper/2302.06100/) (2023)
- [Explaining Legal Concepts with Augmented Large Language Models (GPT-4)](https://mlsanity.com/paper/2306.09525/) (2023)
- [Measuring Law Over Time: A Network Analytical Framework with an Application to Statutes and Regulations in the United States and Germany](https://mlsanity.com/paper/2101.11284/) (2021)
- [From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systems](https://mlsanity.com/paper/2311.04911/) (2023)
- [JusticeBot: A Methodology for Building Augmented Intelligence Tools for Laypeople to Increase Access to Justice](https://mlsanity.com/paper/2308.02032/) (2023)
- [The Legislative Recipe: Syntax for Machine-Readable Legislation](https://mlsanity.com/paper/2108.08678/) (2021)

## Abstract (arXiv)

Legal autonomy - the lawful activity of artificial intelligence agents - can be achieved in one of two ways. It can be achieved either by imposing constraints on AI actors such as developers, deployers and users, and on AI resources such as data, or by imposing constraints on the range and scope of the impact that AI agents can have on the environment. The latter approach involves encoding extant rules concerning AI driven devices into the software of AI agents controlling those devices (e.g., encoding rules about limitations on zones of operations into the agent software of an autonomous drone device). This is a challenge since the effectivity of such an approach requires a method of extracting, loading, transforming and computing legal information that would be both explainable and legally interoperable, and that would enable AI agents to reason about the law. In this paper, we sketch a proof of principle for such a method using large language models (LLMs), expert legal systems known as legal decision paths, and Bayesian networks. We then show how the proposed method could be applied to extant regulation in matters of autonomous cars, such as the California Vehicle Code.