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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2512.04116 |
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| _version_ | 1866911301293309952 |
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| author | Taka, Evdoxia Lawal, Temitope Calder, Muffy Sevegnani, Michele Kotsoglou, Kyriakos McClory-Tiarks, Elizabeth Oswald, Marion |
| author_facet | Taka, Evdoxia Lawal, Temitope Calder, Muffy Sevegnani, Michele Kotsoglou, Kyriakos McClory-Tiarks, Elizabeth Oswald, Marion |
| contents | Commercial or in-house developments of probabilistic AI systems are introduced in policing and the wider criminal justice (CJ) system worldwide, often on a force-by-force basis. We developed a systematic way to characterise probabilistic AI tools across the CJ stages in a form of mapping with the aim to provide a coherent presentation of the probabilistic AI ecosystem in CJ. We use the CJ system in England and Wales as a paradigm. This map will help us better understand the extent of AI's usage in this domain (how, when, and by whom), its purpose and potential benefits, its impact on people's lives, compare tools, and identify caveats (bias, obscured or misinterpreted probabilistic outputs, cumulative effects by AI systems feeding each other, and breaches in the protection of sensitive data), as well as opportunities for future implementations. In this paper we present our methodology for systematically mapping the probabilistic AI tools in CJ stages and characterising them based on the modes of data consumption or production. We also explain how we collect the data and present our initial findings. This research is ongoing and we are engaging with UK Police organisations, and government and legal bodies. Our findings so far suggest a strong reliance on private sector providers, and that there is a growing interest in generative technologies and specifically Large Language Models (LLMs). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04116 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mapping the Probabilistic AI Ecosystem in Criminal Justice in England and Wales Taka, Evdoxia Lawal, Temitope Calder, Muffy Sevegnani, Michele Kotsoglou, Kyriakos McClory-Tiarks, Elizabeth Oswald, Marion Computers and Society Commercial or in-house developments of probabilistic AI systems are introduced in policing and the wider criminal justice (CJ) system worldwide, often on a force-by-force basis. We developed a systematic way to characterise probabilistic AI tools across the CJ stages in a form of mapping with the aim to provide a coherent presentation of the probabilistic AI ecosystem in CJ. We use the CJ system in England and Wales as a paradigm. This map will help us better understand the extent of AI's usage in this domain (how, when, and by whom), its purpose and potential benefits, its impact on people's lives, compare tools, and identify caveats (bias, obscured or misinterpreted probabilistic outputs, cumulative effects by AI systems feeding each other, and breaches in the protection of sensitive data), as well as opportunities for future implementations. In this paper we present our methodology for systematically mapping the probabilistic AI tools in CJ stages and characterising them based on the modes of data consumption or production. We also explain how we collect the data and present our initial findings. This research is ongoing and we are engaging with UK Police organisations, and government and legal bodies. Our findings so far suggest a strong reliance on private sector providers, and that there is a growing interest in generative technologies and specifically Large Language Models (LLMs). |
| title | Mapping the Probabilistic AI Ecosystem in Criminal Justice in England and Wales |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2512.04116 |