Mapping the Probabilistic AI Ecosystem in Criminal Justice in England and Wales
Evdoxia Taka, Temitope Lawal, Muffy Calder, Michele Sevegnani, Kyriakos Kotsoglou, Elizabeth McClory-Tiarks, Marion Oswald

TL;DR
This paper introduces a systematic mapping methodology to characterize probabilistic AI tools across criminal justice stages in England and Wales, highlighting their usage, impact, and emerging trends like LLMs.
Contribution
It presents a novel systematic approach for mapping probabilistic AI in criminal justice, including data collection methods and initial findings on industry reliance and technological trends.
Findings
Strong reliance on private sector AI providers
Growing interest in generative AI and LLMs
Identification of biases and data protection issues
Abstract
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…
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Taxonomy
TopicsEthics and Social Impacts of AI · Criminal Justice and Corrections Analysis · Crime Patterns and Interventions
