How Analysts Use AI in High-Stakes Crime Linkage: An Industrial Study

• URL: http://arxiv.org/abs/2607.08274v1
• Date: Thu, 09 Jul 2026 09:17:25 GMT
• Title: How Analysts Use AI in High-Stakes Crime Linkage: An Industrial Study
• Abstract summary: Crime linkage analysis is used in many countries to identify series of offences that may have been committed by the same individual.<n>Specialist analysts manually search for behavioural and situational connections across large crime databases.<n>To support this work, an Artificial Intelligence (AI)-enabled decision-support tool was co-developed with a UK law enforcement agency.
• Score: 1.3056872812707394
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Crime linkage analysis is used in many countries to identify series of offences that may have been committed by the same individual. In practice, specialist analysts manually search for behavioural and situational connections across large crime databases, an effort that is time-consuming, cognitively demanding, and can involve repeated exposure to disturbing material. To support this work, an Artificial Intelligence (AI)-enabled decision-support tool was co-developed with a UK law enforcement agency to assist analysts in identifying likely crime linkages. This paper reports an industrial evaluation of the crime-linkage tool. We conducted a mixed-methods usability study combining direct observation, eye-tracking, mouse-tracking, and surveys to examine how analysts engage with AI predictions and with the model features presented as explanations. Our findings show that analysts used the AI predictions selectively and frequently validated them against behavioural (non-AI) evidence, reflecting partial trust and an ongoing reliance on established analytical practices. We also found that analysts attended to the presented model features and valued their availability, while identifying opportunities to improve how explanations are presented and integrated into the workflow. Overall, our results highlight the need for AI-enabled decision-support tools to better integrate explanations and traditional analytical methods, and demonstrate the importance of in-situ evaluation for engineering usable and trustworthy AI in high-stakes settings.

Related papers

• Pitfalls in Evaluating Interpretability Agents [91.49742416116635]
  We build an agentic system in which a research agent iteratively designs experiments and refines hypotheses.<n>Our work demonstrates fundamental challenges in evaluating complex automated interpretability systems.
  arXiv  Detail & Related papers  (2026-03-20T16:27:17Z)
• An Explainable Memory Forensics Approach for Malware Analysis [1.2744523252873352]
  Memory forensics is an effective methodology for analyzing living-off-the-land malware.<n>In this paper, we propose an explainable, AI-assisted memory forensics approach.<n>We apply the proposed methodology to both Windows and Android malware.
  arXiv  Detail & Related papers  (2026-02-23T13:30:04Z)
• AI Agents vs. Human Investigators: Balancing Automation, Security, and Expertise in Cyber Forensic Analysis [0.0]
  AI agents are being adopted across digital forensic practices due to their ability to automate processes such as anomaly detection, evidence classification, and behavioral pattern recognition.<n>However, the characteristics that make AI indispensable also introduce notable risks.<n>This study presents a comparative analysis of the effectiveness of the most used AI agent, ChatGPT, and human forensic investigators.
  arXiv  Detail & Related papers  (2026-01-20T23:52:32Z)
• Disagreement as Data: Reasoning Trace Analytics in Multi-Agent Systems [0.0]
  We propose that reasoning traces generated by large language model (LLM) agents constitute a novel and rich form of process data.<n>We apply cosine similarity to reasoning traces to systematically detect, quantify, and interpret disagreements among agents.
  arXiv  Detail & Related papers  (2026-01-18T23:19:49Z)
• Human-AI Collaborative Inductive Thematic Analysis: AI Guided Analysis and Human Interpretive Authority [0.750605871308538]
  This study examines how researchers interact with an Inductive Thematic Analysis GPT (ITA-GPT)<n>ITA-GPT is a purpose-built AI tool designed to support inductive thematic analysis.<n>The study focuses on analytic process rather than substantive findings.
  arXiv  Detail & Related papers  (2026-01-17T00:38:36Z)
• Toward Quantitative Modeling of Cybersecurity Risks Due to AI Misuse [50.87630846876635]
  We develop nine detailed cyber risk models.<n>Each model decomposes attacks into steps using the MITRE ATT&CK framework.<n>Individual estimates are aggregated through Monte Carlo simulation.
  arXiv  Detail & Related papers  (2025-12-09T17:54:17Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [51.26815896167173]
  We present a comprehensive tertiary analysis of PAMI reviews along three complementary dimensions.<n>Our analyses reveal distinctive organizational patterns as well as persistent gaps in current review practices.<n>Finally, our evaluation of state-of-the-art AI-generated reviews indicates encouraging advances in coherence and organization.
  arXiv  Detail & Related papers  (2024-02-20T11:28:50Z)
• PyRCA: A Library for Metric-based Root Cause Analysis [66.72542200701807]
  PyRCA is an open-source machine learning library of Root Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps) It provides a holistic framework to uncover the complicated metric causal dependencies and automatically locate root causes of incidents.
  arXiv  Detail & Related papers  (2023-06-20T09:55:10Z)
• Understanding metric-related pitfalls in image analysis validation [59.15220116166561]
  This work provides the first comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy.
  arXiv  Detail & Related papers  (2023-02-03T14:57:40Z)
• A Prescriptive Learning Analytics Framework: Beyond Predictive Modelling and onto Explainable AI with Prescriptive Analytics and ChatGPT [0.0]
  This study proposes a novel framework that unifies both transparent machine learning as well as techniques for enabling prescriptive analytics. This work practically demonstrates the proposed framework using predictive models for identifying at-risk learners of programme non-completion.
  arXiv  Detail & Related papers  (2022-08-31T00:57:17Z)

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.