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How to evaluate AI technology for investigations

Gary Lloyd | Oct 13,26 | 01:40 EST

Technology

Artificial intelligence is no longer a future concept for law enforcement; it's already integrated into daily tools officers use to extract insights from public records. This article, based on a Police1 webcast, provides a structured framework for agencies to vet AI tools before their adoption in investigations, ensuring they are secure, trustworthy, and supported by transparent citations.

Where does the data actually come from This section emphasizes the critical importance of data sourcing for AI tools in investigations. Unverified AI outputs are compared to anonymous tips, stressing that every data point must be traceable to a named, recently verified source with a clear citation trail. It also highlights the 'garbage in, garbage out' problem, where inconsistent underlying data leads to unreliable conclusions. Agencies are advised to ask vendors for data origin, verification dates, citation trails, and consistency of results over time to ensure evidentiary standards and prevent basing cases on untraceable information. How consumer AI differs from purpose-built AI This section differentiates between general consumer-grade AI tools and purpose-built investigative platforms for law enforcement. Consumer tools lack essential features like data controls, citations, and audit trails, often storing sensitive personal data on uncontrolled servers and failing to disclose data origins. In contrast, purpose-built platforms for investigations pull from curated, verified sources, provide transparent citations for every output, and timestamp information for currency. This distinction is crucial for courtroom defensibility, as a single weak link in data sourcing can undermine an entire case during cross-examination. Can the output hold up in court? This part focuses on the legal defensibility of AI outputs in a courtroom setting. It explains that while an AI hallucination and a properly sourced AI output might appear similar on the surface, only the latter is admissible and verifiable due to its supporting documentation. An unverifiable output has no basis for a judge, whereas a sourced one directly links to specific public records, datasets, and their dates. The section stresses that a robust audit trail, detailing the query, the data pulled in, and the reasoning behind an output, is essential for investigators to explain and defend their findings against increasing scrutiny from defense attorneys. What return on investment can you expect? This section addresses the tangible benefits and return on investment (ROI) of implementing AI in law enforcement investigations. It highlights that officers commonly spend five to ten hours per week on manual research for suspects, witnesses, or persons of interest, a workload that AI can significantly reduce. The panel suggests that the recovered hours can be reallocated to reviewing old reports or working through backlogs. Thomson Reuters CLEAR Investigate customers have reportedly achieved research time reductions of up to 47%, transforming hours of manual lookups into mere minutes. For agencies with limited budgets, it's advised to request real-world usage data from vendors to demonstrate measurable time savings and justify the investment's value. Does it support your documentation and reporting needs? This section emphasizes AI's role in streamlining critical documentation and reporting tasks, which often consume hours that could be dedicated to active casework. AI tools can transform hours of typing into synthesized, professional summaries for briefing notes, incident reports, or watch commander summaries in minutes. Gallardo illustrates how AI can trace a single piece of information, such as a phone number from a robbery victim, to associated individuals, addresses, and prior reports, while maintaining a clear and verifiable audit trail. This ensures every step of an investigation is transparent and defensible, improving case closure and accountability. The text also mentions complementary tools like Social Media Insights and Graylark Raven, which integrate with CLEAR Investigate to further accelerate investigative processes from initial leads to verified locations or registered owners. Ultimately, the right AI tool not only saves time but also provides trusted, scrutinizable information.

Where does the data actually come from

This section emphasizes the critical importance of data sourcing for AI tools in investigations. Unverified AI outputs are compared to anonymous tips, stressing that every data point must be traceable to a named, recently verified source with a clear citation trail. It also highlights the 'garbage in, garbage out' problem, where inconsistent underlying data leads to unreliable conclusions. Agencies are advised to ask vendors for data origin, verification dates, citation trails, and consistency of results over time to ensure evidentiary standards and prevent basing cases on untraceable information.

How consumer AI differs from purpose-built AI

This section differentiates between general consumer-grade AI tools and purpose-built investigative platforms for law enforcement. Consumer tools lack essential features like data controls, citations, and audit trails, often storing sensitive personal data on uncontrolled servers and failing to disclose data origins. In contrast, purpose-built platforms for investigations pull from curated, verified sources, provide transparent citations for every output, and timestamp information for currency. This distinction is crucial for courtroom defensibility, as a single weak link in data sourcing can undermine an entire case during cross-examination.

Can the output hold up in court?

This part focuses on the legal defensibility of AI outputs in a courtroom setting. It explains that while an AI hallucination and a properly sourced AI output might appear similar on the surface, only the latter is admissible and verifiable due to its supporting documentation. An unverifiable output has no basis for a judge, whereas a sourced one directly links to specific public records, datasets, and their dates. The section stresses that a robust audit trail, detailing the query, the data pulled in, and the reasoning behind an output, is essential for investigators to explain and defend their findings against increasing scrutiny from defense attorneys.

What return on investment can you expect?

This section addresses the tangible benefits and return on investment (ROI) of implementing AI in law enforcement investigations. It highlights that officers commonly spend five to ten hours per week on manual research for suspects, witnesses, or persons of interest, a workload that AI can significantly reduce. The panel suggests that the recovered hours can be reallocated to reviewing old reports or working through backlogs. Thomson Reuters CLEAR Investigate customers have reportedly achieved research time reductions of up to 47%, transforming hours of manual lookups into mere minutes. For agencies with limited budgets, it's advised to request real-world usage data from vendors to demonstrate measurable time savings and justify the investment's value.

Does it support your documentation and reporting needs?

This section emphasizes AI's role in streamlining critical documentation and reporting tasks, which often consume hours that could be dedicated to active casework. AI tools can transform hours of typing into synthesized, professional summaries for briefing notes, incident reports, or watch commander summaries in minutes. Gallardo illustrates how AI can trace a single piece of information, such as a phone number from a robbery victim, to associated individuals, addresses, and prior reports, while maintaining a clear and verifiable audit trail. This ensures every step of an investigation is transparent and defensible, improving case closure and accountability. The text also mentions complementary tools like Social Media Insights and Graylark Raven, which integrate with CLEAR Investigate to further accelerate investigative processes from initial leads to verified locations or registered owners. Ultimately, the right AI tool not only saves time but also provides trusted, scrutinizable information.