Mapping the Probabilistic AI Ecosystem in Criminal Justice in England and Wales

• URL: http://arxiv.org/abs/2512.04116v1
• Date: Mon, 01 Dec 2025 14:56:53 GMT
• Title: Mapping the Probabilistic AI Ecosystem in Criminal Justice in England and Wales
• Authors: Evdoxia Taka, Temitope Lawal, Muffy Calder, Michele Sevegnani, Kyriakos Kotsoglou, Elizabeth McClory-Tiarks, Marion Oswald,
• Abstract summary: We present our methodology for systematically mapping the probabilistic AI tools in criminal justice (CJ) stages.<n>We also explain how we collect the data and present our initial findings.<n>This research is ongoing and we are engaging with UK Police organisations, and government and legal bodies.
• Score: 0.5863360388454261
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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).

Related papers

• AI Automatons: AI Systems Intended to Imitate Humans [54.19152688545896]
  There is a growing proliferation of AI systems designed to mimic people's behavior, work, abilities, likenesses, or humanness.<n>The research, design, deployment, and availability of such AI systems have prompted growing concerns about a wide range of possible legal, ethical, and other social impacts.
  arXiv  Detail & Related papers  (2025-03-04T03:55:38Z)
• A Systems Thinking Approach to Algorithmic Fairness [0.0]
  We use systems thinking to help policymakers understand the complex trade-offs that exist from different types of fairness policies.<n>We can use systems thinking to help policymakers on both sides of the political aisle to understand the complex trade-offs that exist from different types of fairness policies.
  arXiv  Detail & Related papers  (2024-12-21T14:21:33Z)
• Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks [55.2480439325792]
  This paper critically examines the European Union's Artificial Intelligence Act (EU AI Act) Uses insights from Alignment Theory (AT) research, which focuses on the potential pitfalls of technical alignment in Artificial Intelligence. As we apply these concepts to the EU AI Act, we uncover potential vulnerabilities and areas for improvement in the regulation.
  arXiv  Detail & Related papers  (2024-10-10T17:38:38Z)
• Participatory Approaches in AI Development and Governance: A Principled Approach [9.271573427680087]
  This paper forms the first part of a two-part series on participatory governance in AI. It advances the premise that a participatory approach is beneficial to building and using more responsible, safe, and human-centric AI systems.
  arXiv  Detail & Related papers  (2024-06-03T09:49:42Z)
• Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing [74.58071278710896]
  generative AI has attracted much attention from both academic and industrial fields. Secure and privacy-preserving mobile crowdsensing (SPPMCS) has been widely applied in data collection/ acquirement.
  arXiv  Detail & Related papers  (2024-05-17T04:00:58Z)
• Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination [1.4305544869388402]
  Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making. The integration of KGs with neuronal learning is currently a topic of active research. This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination.
  arXiv  Detail & Related papers  (2023-10-30T12:51:52Z)
• Predictable Artificial Intelligence [77.1127726638209]
  This paper introduces the ideas and challenges of Predictable AI.<n>It explores the ways in which we can anticipate key validity indicators of present and future AI ecosystems.<n>We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems.
  arXiv  Detail & Related papers  (2023-10-09T21:36:21Z)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)
• AI Assurance using Causal Inference: Application to Public Policy [0.0]
  Most AI approaches can only be represented as "black boxes" and suffer from the lack of transparency. It is crucial not only to develop effective and robust AI systems, but to make sure their internal processes are explainable and fair.
  arXiv  Detail & Related papers  (2021-12-01T16:03:06Z)
• Trustworthy AI Inference Systems: An Industry Research View [58.000323504158054]
  We provide an industry research view for approaching the design, deployment, and operation of trustworthy AI inference systems. We highlight opportunities and challenges in AI systems using trusted execution environments. We outline areas of further development that require the global collective attention of industry, academia, and government researchers.
  arXiv  Detail & Related papers  (2020-08-10T23:05:55Z)
• Trustworthy AI in the Age of Pervasive Computing and Big Data [22.92621391190282]
  We formalise the requirements of trustworthy AI systems through an ethics perspective. After discussing the state of research and the remaining challenges, we show how a concrete use-case in smart cities can benefit from these methods.
  arXiv  Detail & Related papers  (2020-01-30T08:09:31Z)
• Bias in Data-driven AI Systems -- An Introductory Survey [37.34717604783343]
  This survey focuses on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful Machine Learning (ML) algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features like race, sex, etc.
  arXiv  Detail & Related papers  (2020-01-14T09:39:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.