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

2026-07-09

AI Summary

This study addresses the challenges of crime linkage analysis, a process traditionally reliant on manual identification of behavioral and contextual connections across large volumes of cases—tasks that are time-intensive, cognitively demanding, and potentially distressing due to exposure to traumatic content. In collaboration with UK law enforcement agencies, the research presents the first empirical evaluation of an AI-assisted decision support tool within a real-world, high-stakes operational environment. Employing a mixed-methods approach—including eye-tracking, mouse-tracking, direct observation, and questionnaires—the study investigates how analysts interact with AI-generated predictions and their accompanying explainable features. Findings indicate that while analysts value feature-level explanations from the AI, they predominantly anchor their judgments in conventional behavioral evidence. The results reveal a selective adoption and verification strategy toward AI outputs and suggest design refinements that more tightly integrate explainability into existing analytical workflows to enhance the tool’s usability and trustworthiness.

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.

Problem

• crime linkage
• analyst workflow
• high-stakes decision-making
• behavioral evidence
• disturbing material

Innovation

• crime linkage analysis
• AI-enabled decision support
• explainable AI
• mixed-methods evaluation
• human-AI collaboration