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Autori principali: Calder, Muffy, Oswald, Marion, McClory-Tiarks, Elizabeth, Sevegnani, Michele, Taka, Evdoxia
Natura: Preprint
Pubblicazione: 2026
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Accesso online:https://arxiv.org/abs/2603.18116
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author Calder, Muffy
Oswald, Marion
McClory-Tiarks, Elizabeth
Sevegnani, Michele
Taka, Evdoxia
author_facet Calder, Muffy
Oswald, Marion
McClory-Tiarks, Elizabeth
Sevegnani, Michele
Taka, Evdoxia
contents There is growing interest in the use of Large Language Models (LLMs) in policing, but there are potential risks. We have developed a practical approach to identifying risks, grounded in the policing and legal system of England and Wales. We identify 15 policing tasks that could be implemented using LLMs and 17 risks from their use, then illustrate with over 40 examples of impact on case progression. As good practice is agreed, many risks could be reduced. But this requires effort: we need to address these risks in a timely manner and define system wide impacts and benefits.
format Preprint
id arxiv_https___arxiv_org_abs_2603_18116
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Responsible AI in criminal justice: LLMs in policing and risks to case progression
Calder, Muffy
Oswald, Marion
McClory-Tiarks, Elizabeth
Sevegnani, Michele
Taka, Evdoxia
Computers and Society
There is growing interest in the use of Large Language Models (LLMs) in policing, but there are potential risks. We have developed a practical approach to identifying risks, grounded in the policing and legal system of England and Wales. We identify 15 policing tasks that could be implemented using LLMs and 17 risks from their use, then illustrate with over 40 examples of impact on case progression. As good practice is agreed, many risks could be reduced. But this requires effort: we need to address these risks in a timely manner and define system wide impacts and benefits.
title Responsible AI in criminal justice: LLMs in policing and risks to case progression
topic Computers and Society
url https://arxiv.org/abs/2603.18116