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Human-AI Interaction for Time-Critical Sensemaking in Missing Persons Investigations

A participatory study with Police Scotland search officers tested AI-supported summarisation, fact extraction and visualisation for fragmented missing-person information, stressing accuracy, context and user-centred workflow design.

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  • Publisher(s): Association for Computing Machinery

  • Publication date: 2026

  • Country: United Kingdom

Why it matters: It supports using AI to surface and organise information, not to make the risk or search decision. The human need is to retain provenance, uncertainty and enough context to challenge an apparently tidy summary.

Limitations: The evidence concerns investigative information work, not live SAR outcomes. The source describes testing with professionals and anonymised material; it does not validate any tool as a risk-scoring or search-planning system.

Training influence: Use an AI-generated incident summary alongside the source material. Assess whether planners verify key facts, identify omissions and document what still needs to be established before tasking resources.

Training sections: Initial Incident Picture; information gathering; subject assessment; scenario development; decision logging; technology governance.

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