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This study evaluates an AI-enabled decision-support tool designed to assist analysts in crime linkage analysis, which is crucial for identifying connections between offenses. Through a mixed-methods usability study involving direct observation and tracking techniques, the research reveals that while analysts selectively utilize AI predictions, they often validate these against traditional behavioral evidence, indicating a partial trust in the AI system. The findings underscore the necessity for better integration of AI explanations into existing workflows to enhance usability and trust in high-stakes environments.
Analysts trust AI predictions but still rely heavily on traditional methods, highlighting a critical gap in AI integration for crime linkage analysis.
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.
