Justifiable Artificial Intelligence: Engineering Large Language Models for Legal Applications

TL;DR

This paper explores how Large Language Models can be adapted for legal applications by emphasizing justifiability over explainability, aiming to improve trustworthiness and accountability in their outputs.

Contribution

It introduces the concept of Justifiable Artificial Intelligence for legal LLMs, focusing on evidence-based validation to enhance trust and accountability.

Findings

Evidence-based validation can improve trust in LLM outputs.

Justifiable AI offers a new framework for legal applications.

Accountability mechanisms can mitigate misinformation.

Abstract

In this work, I discuss how Large Language Models can be applied in the legal domain, circumventing their current drawbacks. Despite their large success and acceptance, their lack of explainability hinders legal experts to trust in their output, and this happens rightfully so. However, in this paper, I argue in favor of a new view, Justifiable Artificial Intelligence, instead of focusing on Explainable Artificial Intelligence. I discuss in this paper how gaining evidence for and against a Large Language Model's output may make their generated texts more trustworthy - or hold them accountable for misinformation.