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

2025-12-01

AI Summary

This study examines the current deployment and associated risks of probabilistic artificial intelligence (AI) tools across the criminal justice system in England and Wales. Key problems include ambiguous scope of AI application, latent algorithmic bias, feedback amplification across interconnected systems, weak data privacy safeguards, and excessive reliance on private-sector vendors. To address these, we develop the first end-to-end mapping framework for probabilistic AI tools in this domain, integrating systematic literature review with primary fieldwork—including interviews with practitioners—and specifically tracking emerging generative AI and large language model applications in policing and judicial decision support. Our findings reveal: dominant technical control by commercial suppliers; frequent oversimplification or misinterpretation of probabilistic outputs; systemic coupling risks arising from cross-agency data flows; and structural deficiencies in sensitive data protection. The study provides empirically grounded insights and methodological tools to detect algorithmic bias, strengthen AI governance, advance transparency-by-design, and reinforce public accountability.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Computer Vision: Bias, Fairness & Privacy
• Reasoning under Uncertainty: Probabilistic Programming

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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).

Problem

• Mapping probabilistic AI tools in criminal justice stages
• Characterizing AI usage extent, purpose, and potential impacts
• Identifying caveats like bias and data breaches in AI systems

Innovation

• Developed systematic mapping of probabilistic AI tools
• Characterized tools by data consumption and production modes
• Engaged with police, government, and legal bodies for data