Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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 study offers a systematic framework to map probabilistic AI tools within England and Wales' criminal justice system, addressing how, when, and by whom AI is used across CJ stages. It develops a two-part methodology: a stage-based CJ framework and a detailed AI-tool taxonomy (inference modes and input-output modalities) to classify tools, supported by online sources and stakeholder interviews. The initial findings reveal a heavy reliance on third-party tools, a concentration of AI activity in early CJ stages, and rising interest in generative technologies and LLMs, with implications for privacy, bias, and governance. The work culminates in a data repository and plans for a public interface to enhance transparency, comparability, and responsible deployment of AI in CJ.

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).

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

TL;DR

This study offers a systematic framework to map probabilistic AI tools within England and Wales' criminal justice system, addressing how, when, and by whom AI is used across CJ stages. It develops a two-part methodology: a stage-based CJ framework and a detailed AI-tool taxonomy (inference modes and input-output modalities) to classify tools, supported by online sources and stakeholder interviews. The initial findings reveal a heavy reliance on third-party tools, a concentration of AI activity in early CJ stages, and rising interest in generative technologies and LLMs, with implications for privacy, bias, and governance. The work culminates in a data repository and plans for a public interface to enhance transparency, comparability, and responsible deployment of AI in CJ.

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).

Paper Structure

This paper contains 13 sections, 1 figure, 3 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Staging of the CJ cystem in England & Wales
  4. Taxonomy of AI tools
  5. Description of the mapping data
  6. Data collection
  7. Online resources
  8. Interviews
  9. An instance of the map
  10. Research Questions (RQs)
  11. Limitations
  12. Findings
  13. Conclusions

Figures (1)

  • Figure 1: Sample of AI tools currently used in the Criminal Justice (CJ) system in England and Wales. The purpose and taxonomy of each tool are stated below the name of each tool. The colour of each tool's box indicates the deployment stage of the tool: green for deployed, yellow for trialled, and pink for experimental. Shaded boxes indicate tools not used in the corresponding stages of CJ.