Justifiable Artificial Intelligence: Engineering Large Language Models for Legal Applications
This addresses the issue of trust and accountability for legal experts using LLMs, but it appears incremental as it builds on existing explainability concepts.
The paper tackles the problem of applying Large Language Models (LLMs) in the legal domain by proposing Justifiable Artificial Intelligence to address their lack of explainability, aiming to make generated texts more trustworthy or hold them accountable for misinformation.
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.