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 (2403.18537v1)

Abstract: 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 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.

Extracting, Transforming, Loading, and Computing Legal Information with AI: A Comprehensive Approach

Introduction to the Study

The paper presents a methodological innovation in the intersection of AI and legal reasoning. It outlines a framework that combines LLMs, expert systems, and Bayesian networks to process legal texts for the purpose of embedding legal reasoning within AI systems, with a practical application to autonomous vehicles. This work underlines the significance of creating AI systems that can autonomously interpret and comply with legal regulations, ensuring both interoperability across jurisdictions and explainability of the AI's decision-making processes.

Challenges in Legal Autonomy for AI

Two primary hurdles exist in embedding legal reasoning within AI systems: interoperability and explainability.

• Interoperability: This challenge involves designing AI systems that can adapt to and comply with various legal jurisdictions. The paper exemplifies this through the use of autonomous vehicles, which must operate under different legal systems across countries and regions. The paper emphasizes the need for a unified system that can extract, transform, load, and compute (ETLC) legal information from diverse legal texts into a form that AI systems can utilize effectively.
• Explainability: This refers to the ability of AI systems to justify their decisions in human-understandable terms, especially when those decisions have legal implications. Given the potential for AI decisions to cause harm or legal disputes, the paper stresses the importance of creating AI systems whose reasoning processes are transparent and can be audited post-hoc.

Proposed Solution: ETLC Framework

The paper proposes a novel ETLC framework that integrates three components: LLMs for extracting and transforming legal texts into a structured format, expert systems to create logical decision paths from these texts, and Bayesian networks to enable AI systems to make decisions based on this structured legal information.

• Decision Paths and Expert Systems: By encoding legal texts into decision paths — a structured series of questions and conditions — the paper leverages expert systems to formalize any legal rule. This approach ensures that the AI's decision-making process is aligned with legal reasoning, enhancing explainability.
• LLMs: The use of LLMs for transforming legal texts into structured decision paths is highlighted as a method for achieving interoperability across jurisdictions. The LLMs automate the extraction and structuring of legal information, reducing the manual effort required to adapt AI systems to new or amended legal texts.
• Bayesian Networks: To address the inherent uncertainties in legal reasoning — such as open-textured terms and the balance of probabilities in legal standards — the paper introduces Bayesian networks. These networks complement the logical decision paths by incorporating probabilistic reasoning, allowing AI systems to evaluate and act upon legal criteria with quantifiable confidence levels.

Practical Application and Implications

The applicability of this ETLC framework is demonstrated through a case paper on autonomous vehicles and their compliance with the California Vehicle Code. This real-world application illuminates the framework's potential to ensure that AI systems can autonomously make legally compliant decisions — a key step towards the broader goal of legal autonomy for AI.

Beyond the technical accomplishments, the paper speculates on the broader implications of this research. It discusses the potential for this ETLC framework to serve as a foundational approach for regulating AI systems more effectively, by embedding legal compliance directly into their operational logic. The framework's emphasis on explainability and interoperability aligns with ongoing discussions in AI ethics and law regarding accountability, transparency, and the harmonization of AI systems with societal values.

Conclusion

The paper posits a comprehensive ETLC framework as a viable solution to the dual challenges of achieving legal interoperability and explainability in AI systems. By seamlessly integrating LLMs, expert systems, and Bayesian networks, the framework promises to equip AI systems with the ability to autonomously navigate and comply with complex legal landscapes. The practical application to autonomous vehicles exemplifies the framework's potential, setting the stage for future research and development in the pursuit of legally autonomous AI systems.