Potentials and Challenges of Large Language Models (LLMs) in the Context of Administrative Decision-Making

Abstract

Large Language Models (LLMs) could facilitate both more efficient administrative decision-making on the one hand, and better access to legal explanations and remedies to individuals concerned by administrative decisions on the other hand. However, it is an open research question of how performant such domain-specific models could be. Furthermore, they pose legal challenges, touching especially upon administrative law, fundamental rights, data protection law, AI regulation, and copyright law. The article provides an introduction into LLMs, outlines potential use cases for such models in the context of administrative decisions, and presents a non-exhaustive introduction to practical and legal challenges that require in-depth interdisciplinary research. A focus lies on open practical and legal challenges with respect to legal reasoning through LLMs. The article points out under which circumstances administrations can fulfil their duty to provide reasons with LLM-generated reasons. It highlights the importance of human oversight and the need to design LLM-based systems in a way that enables users such as administrative decision-makers to effectively oversee them. Furthermore, the article addresses the protection of training data and trade-offs with model performance, bias prevention and explainability to highlight the need for interdisciplinary research projects.