The Official Publication of the Ontario Association of Chiefs of Police 17 surface potentially relevant informa- tion – identifying patterns, grouping similar material, summarizing large datasets or highlighting items that may warrant closer human attention – so investigators can use their exper- tise to focus on analysis rather than exhaustive manual review. For example, an investigator quer- ies the AI to ask if there is evidence of narcotics distribution found on a cellphone extraction. The AI tool may return a series of images or com- munications it believes to be about narcotics distribution. The investigator then examines the highlighted images and chats, interpreting the data to further the investigation. Investigators still review the underlying source evidence. They read the messages. They view the images. They assess relevance and context. The AI tool understands the context of data, but it does not interpret mean- ing – it points investigators toward material that may matter. Before AI, the investigator would have to pore over hundreds of thou- sands of images and conversations to arrive at the same output. In both cases, the investigative work remains the same. The difference is time, efficiency and consistency. In other words, AI is being used to shrink the haystack, not decide what the evidence means. From a leadership perspective, this is not a technology issue – it is an operational one. EFFICIENCY AND BUDGETS Efficiency in this context is not about convenience; it is about public safety and stewardship of limited resources. When digital evidence reviews take weeks or months longer than neces- sary, the result is predictable: stalled investigations, increased strain on investigators, growing pressure on already limited budgets and delayed justice for victims. Digital evidence backlogs grow not because investiga- tors lack skill, but because the volume of data outpaces human capacity. AI-assisted review changes this equation by accelerating initial evi- dence triage, allowing investigators to move more quickly from data collection to analysis, prioritizing cases more effectively and reducing unnecessary delays. For police lead- ers managing budgets and staffing pressures, this matters. AI is not a cost cutting shortcut – it is a way to align investigative capability with modern evidentiary reality without proportion- ally increasing headcount. PUBLIC TRUST DEPENDS ON TRANSPARENCY Community trust in policing is shaped not only by what decisions are made, but how they are made. Poorly gov- erned AI raises legitimate concerns: opacity, over reliance on automation or technology that appears to replace human judgment. However, avoiding AI altogether does not eliminate risk – it creates new risks, which could include missed evidence, investigative inconsistency or investigator burnout. An important leadership respons- ibility is to provide investigators and frontline officers the tools to effect- ively do their work. In modern times, this encompasses AI tools purposely built for investigations, as well as ensuring they are deployed transpar- ently and conservatively, with clear boundaries and human accountability. This includes being able to explain – at a high level – what AI is doing in investigations – and just as import- antly, what it is not doing. AI does not replace investigative judgment. It does not operate autonomously. It does not remove human responsibility. Clear messaging around this dis- tinction is essential for maintaining public confidence. RETHINKING “EXPLAINABILITY” Concerns about “explainability” fre- quently dominate AI discussions in policing, but they are often framed inaccurately. In practice, most officers and investigators cannot explain the internal mechanics of many technol- ogies they already rely on in court – from breath analysis tools to radar systems to complex forensic software. What matters to policing – and to the justice system as a whole – is not that every investigator can explain how an algorithm functions internally, but that: • investigators verify and validate the underlying evidence; • the reliability of the AI tools has been tested and evaluated; and • the outputs are contextualized and weighed by humans. The emphasis on testing and docu- mented performance mirrors guidance recently published by international policing and justice bodies, including the IACP, which consistently identify human oversight and empirical valid- ation – not technical transparency – as the foundation of defensible AI use in law enforcement. The more meaningful leadership question is not “Can my investigators explain how AI works internally?” but rather: • Has this technology been rigorously tested? • Do we understand its limitations? • Are we using it appropriately and conservatively? TESTING, VALIDATION, ERROR RATES One of the strongest arguments for the responsible use of AI in digital forensics is that it can be empirically evaluated. AI features can be tested against known datasets, producing measurable performance metrics and documented error rates. This allows leaders to contextualize outputs and make informed decisions about when and how results should be relied upon. By contrast, there is currently no empirical data measuring how accur- ate human investigators are when reviewing massive digital datasets. Variability, fatigue and inconsistency have always existed in manual review – but have largely gone unmeasured.
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