Potentials and Challenges of Large Language Models (LLMs) in the Context of Administrative Decision-Making
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 su...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2025-03-01
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| Series: | European Journal of Risk Regulation |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S1867299X24000990/type/journal_article |
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| author | Paulina Jo Pesch Herwig C.H. Hofmann Felix Pflücke |
| author_facet | Paulina Jo Pesch Herwig C.H. Hofmann Felix Pflücke |
| author_sort | Paulina Jo Pesch |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6bb62c401e28452984a8a657ca74edd0 |
| institution | Kabale University |
| issn | 1867-299X 2190-8249 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | European Journal of Risk Regulation |
| spelling | doaj-art-6bb62c401e28452984a8a657ca74edd02025-08-20T13:03:13ZengCambridge University PressEuropean Journal of Risk Regulation1867-299X2190-82492025-03-0116769510.1017/err.2024.99Potentials and Challenges of Large Language Models (LLMs) in the Context of Administrative Decision-MakingPaulina Jo Pesch0https://orcid.org/0000-0002-5508-2918Herwig C.H. HofmannFelix PflückeSchool of Law, FAU Erlangen-Nürnberg, Erlangen, GermanyLarge 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.https://www.cambridge.org/core/product/identifier/S1867299X24000990/type/journal_articleadministrative decision-makingartificial intelligence (AI)automated decision-makinglarge language models (LLMs) |
| spellingShingle | Paulina Jo Pesch Herwig C.H. Hofmann Felix Pflücke Potentials and Challenges of Large Language Models (LLMs) in the Context of Administrative Decision-Making European Journal of Risk Regulation administrative decision-making artificial intelligence (AI) automated decision-making large language models (LLMs) |
| title | Potentials and Challenges of Large Language Models (LLMs) in the Context of Administrative Decision-Making |
| title_full | Potentials and Challenges of Large Language Models (LLMs) in the Context of Administrative Decision-Making |
| title_fullStr | Potentials and Challenges of Large Language Models (LLMs) in the Context of Administrative Decision-Making |
| title_full_unstemmed | Potentials and Challenges of Large Language Models (LLMs) in the Context of Administrative Decision-Making |
| title_short | Potentials and Challenges of Large Language Models (LLMs) in the Context of Administrative Decision-Making |
| title_sort | potentials and challenges of large language models llms in the context of administrative decision making |
| topic | administrative decision-making artificial intelligence (AI) automated decision-making large language models (LLMs) |
| url | https://www.cambridge.org/core/product/identifier/S1867299X24000990/type/journal_article |
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