[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83359-en":3,"doc-seo-83359-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83359,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","How Analysts Use AI in High Stakes Crime Linkage An Industrial Study","Crime linkage analysis helps identify series of offences that may involve the same individual, but manual searches across large crime databases are slow, cognitively taxing, and can expose analysts to disturbing material. This paper reports an industrial evaluation of an AI-enabled decision-support tool co-developed with UK law enforcement. A mixed-methods usability study with observation, eye-tracking, mouse-tracking, and surveys shows analysts use AI predictions selectively, validate them with non-AI behavioural evidence, and value explanation features while requesting improved integration into workflow.","How Analysts Use AI in High-Stakes Crime Linkage: An Industrial  \nStudy  \nJessica Woodhams  \nUniversity of Birmingham, UK [j.woodhams@bham.ac.uk](j.woodhams@bham.ac.uk)  \nFahim Ahmed  \nImperial College London, UK [fahim.ahmed22@imperial.ac.uk](fahim.ahmed22@imperial.ac.uk)  \nArkady Konovalov  \nUniversity of Birmingham, UK[a.konovalov@bham.ac.uk](a.konovalov@bham.ac.uk)  \nAmy Burrell  \nUniversity of Birmingham, UK[a.burrell@bham.ac.uk](a.burrell@bham.ac.uk)  \nMatthew Tonkin  \nUniversity of Leicester, UK [mjt46@leicester.ac.uk](mjt46@leicester.ac.uk)  \nSteven Frisson  \nUniversity of Birmingham, UK [s.frisson@bham.ac.uk](s.frisson@bham.ac.uk)  \nWanyin Li University of Reading, UK [wanyin.li@reading.ac.uk](wanyin.li@reading.ac.uk)  \nJan Lemeire  \nVrije Universiteit, Belgium [jan.lemeire@vub.be](jan.lemeire@vub.be)  \nMark Webb  \nNational Crime Agency, UK [mark.webb@nca.gov.uk](mark.webb@nca.gov.uk)  \narXiv :2607 .08274v 1 [ cs .HC] 9 Jul 2026  \nSarah Galambos National Crime Agency, UK [sarah.galambos@nca.gov.uk](sarah.galambos@nca.gov.uk)  \nVesna Nowack  \nImperial College London, UK [v.nowack@imperial.ac.uk](v.nowack@imperial.ac.uk)  \nDalal Alrajeh Imperial College London, UK [dalal.alrajeh04@imperial.ac.uk](dalal.alrajeh04@imperial.ac.uk)  \nAbstract  \nCrime 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 analystsin identifying likely crime linkages.  \nThis 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.  \nKeywords  \nArtificial Intelligence, crime linkage, decision making, usability study  \n1 Introduction  \nSerial crimes, in which an offender commits multiple offences against different victims over time, pose particular challenges for investigators attempting to identify and connect linked incidents.  \nOne major challenge is that many criminals do not leave physical forensic evidence at crime scenes, making it hard to later link crimes and establish a series [3] . Specialist units of highly trained expert analysts exist in different countries of the world who, instead, use behavioural information about the commission of the crime to identify potential linked series. This practice is called crime linkage.  \nOver time, databases of crime-related data grow to hold the behavioural details oftens of thousands of crimes. For example, the UK’s database for a stranger sexual offences contains data related to around 37,000 cases, including information about where and when offences happened, as well as the behaviour demonstrated by the offender during the crime [36] .  \nAnalysts, therefore, have the challenging task of searching within these databases for crimes likely","cbCaiqz6FTvmdM3U","https://ap.wps.com/l/cbCaiqz6FTvmdM3U","pdf",2838103,3,1,12,"English","en",105,"# Introduction\n## Serial crime challenges\n## Crime linkage as behavioural comparison\n## Need for decision-support tools\n## Communication of AI predictions and explanations","[{\"question\":\"What problem does crime linkage analysis address?\",\"answer\":\"It identifies series of offences that may have been committed by the same individual by connecting incidents through behavioural information rather than relying on physical forensic evidence.\"},{\"question\":\"How was the AI-enabled crime linkage tool evaluated?\",\"answer\":\"The study used a mixed-methods usability approach combining direct observation, eye-tracking, mouse-tracking, and surveys to assess how analysts interact with AI predictions and explanation features.\"},{\"question\":\"How did analysts use AI predictions in practice?\",\"answer\":\"Analysts used AI predictions selectively and frequently checked them against non-AI behavioural evidence, indicating partial trust and reliance on established analytical practices.\"}]",1784186982,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"how-analysts-use-ai-in-high-stakes-crime-linkage-an-industrial-study","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/how-analysts-use-ai-in-high-stakes-crime-linkage-an-industrial-study/83359/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does crime linkage analysis address?","Question",{"text":75,"@type":76},"It identifies series of offences that may have been committed by the same individual by connecting incidents through behavioural information rather than relying on physical forensic evidence.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the AI-enabled crime linkage tool evaluated?",{"text":80,"@type":76},"The study used a mixed-methods usability approach combining direct observation, eye-tracking, mouse-tracking, and surveys to assess how analysts interact with AI predictions and explanation features.",{"name":82,"@type":73,"acceptedAnswer":83},"How did analysts use AI predictions in practice?",{"text":84,"@type":76},"Analysts used AI predictions selectively and frequently checked them against non-AI behavioural evidence, indicating partial trust and reliance on established analytical 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