Why Avoid Generative Legal AI Systems? Hallucination, Overreliance, and their Impact on Explainability

TL;DR

This paper highlights the risks of hallucination and overreliance in generative legal AI systems, emphasizing their impact on explainability and the need for restraint in legal applications.

Contribution

It defines Generative Legal AI (GLAI), analyzes its risks, and argues for cautious deployment due to challenges in explainability and potential threats to judicial independence.

Findings

Generative Legal AI models often produce hallucinations, compromising factual accuracy.

Overreliance on GLAI can obscure reasoning, reducing transparency.

Lack of human scrutiny mechanisms weakens legal accountability.

Abstract

This article argues that the deployment of generative AI systems in legal profession requires strong restraint due to the critical risks of hallucination and overreliance. Central to this analysis is the definition of Generative Legal AI (GLAI), an umbrella term for systems specifically adapted for the legal domain which is ranging from document drafting to decision support in criminal justice. Unlike traditional AI, GLAI models are built on architectures designed for statistical token prediction rather than legal reasoning, often leading to confabulations where the system prioritizes linguistic fluency over factual accuracy. These hallucinations obscure the reasoning process, while the persuasive, human-like nature of the output encourages professional overreliance. The paper situates these dynamics within the framework of European AI governance, arguing that the interaction between fabricated data and automation bias fundamentally weakens the principle of explainability. The article concludes that without effective mechanisms for meaningful human scrutiny, the routine adoption of GLAI poses significant challenges to judicial independence and the protection of fundamental rights.