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Dr. Kim Rossmo’s presentation provided a critical and thought-provoking examination of criminal investigative failures, with a strong grounding in the context of Missing and Murdered Indigenous Women and Girls (MMIWG) cases. He emphasized that investigative failures are not isolated incidents, but often systemic, stemming from flawed thinking, missed learning opportunities, and organizational pressures. These failures manifest in ignored crimes, unsolved cases, and wrongful convictions – each carrying profound consequences for individuals and public trust in policing.

Drawing on research and data, Dr. Rossmo highlighted the prevalence and causes of wrongful convictions. In the United States, an estimated 1–5% of homicide and sexual assault cases involve wrongful convictions. The Innocence Project, for example, has documented over 205 DNA-based exonerations, with individuals spending an average of 14 years in prison, with approximately 70% of whom are minorities. He outlined the primary contributing factors: false confessions, unreliable informants, flawed forensic practices, eyewitness errors, and broader investigative mistakes. While these tools; witness testimony, confessions, and physical evidence, are essential to solving crimes, they each carry inherent risks that must be carefully managed.

A central theme of the presentation was the danger of cognitive biases and flawed reasoning in investigations. Dr. Rossmo explained how confirmation bias, tunnel vision, and reliance on cognitive shortcuts (heuristics) can lead investigators to prematurely fixate on a suspect or interpretation of evidence. He stressed the importance of moving away from black-and-white thinking and recognizing that assumptions can quickly become treated as fact. Probability language such as “likely,” “reasonable,” or “cannot be excluded”, can further compound errors, especially when misunderstood or misapplied. He also discussed how individuals are prone to seeing patterns in randomness (pareidolia), reinforcing false conclusions.

Beyond individual decision-making, Dr. Rossmo examined organizational factors that contribute to investigative failures. Policing structures – often hierarchical and semi-military, can foster groupthink, discourage dissent, and amplify the influence of dominant personalities. Combined with operational pressures, fatigue, and the urgency of high-stakes cases, these conditions can impair critical thinking and lead to rushed or flawed decisions. Policies, supervision, and structured review processes were presented as essential safeguards to mitigate these risks.

Finally, Dr. Rossmo underscored the importance of context when interpreting evidence, particularly DNA, and referenced research from the National Institute of Justice and case studies such as the David Camm investigation to illustrate real-world consequences. His presentation ultimately called for a more reflective, evidence-informed approach to investigations; one that prioritizes critical thinking, acknowledges human fallibility, and actively works to prevent error. By learning from past failures, policing can better position itself to deliver fair, accurate, and just outcomes in the future.

Professor Benjamin Perrin’s presentation explored the rapidly evolving role of artificial intelligence (AI) in policing, emphasizing both it’s potential and significant risks. He began by acknowledging that no single group currently holds all the expertise needed to guide AI adoption effectively. Instead, he advocated for interdisciplinary collaboration among legal experts, technologists, police practitioners, oversight bodies, and privacy commissioners to ensure responsible implementation in high-stakes public safety contexts.

A key concern raised throughout the presentation was the lack of independent, scientific validation of many AI tools currently being used or considered by police agencies. While some technologies have demonstrated improvements in efficiency or investigative capacity under controlled conditions, evidence of their real-world accuracy, reliability, and effectiveness remains limited. At the same time, Perrin highlighted widely documented risks including bias, errors, and privacy intrusions – many of which are not yet fully mitigated. The absence of clear, policing-specific legal frameworks and the lack of Charter-based judicial scrutiny further compound these concerns.

Professor Perrin provided practical examples to illustrate these challenges, including facial recognition technologies, automated license plate readers, and AI-assisted report-writing tools such as AXON’s Draft One, which is currently being piloted in Canada. He discussed the “verification-value paradox,” where AI-generated outputs may save time but require extensive review to ensure accuracy. Issues such as automation bias, where users assume AI outputs are correct, along with documented errors (including fabricated details or “hallucinations”), raise serious reliability and disclosure concerns. He also referenced real-world cases, such as State v. Carr (2024) and R v. J.L. (2000 SCC 51), underscoring the legal scrutiny applied to novel forms of evidence.

The presentation also examined significant privacy and ethical implications. Perrin pointed to the Clearview AI case in Canada, where billions of images were scraped without consent to create a facial recognition database – an action found to violate privacy laws. He stressed the importance of understanding how AI systems are trained, including potential biases in datasets, and questioned the legal authority underpinning the use of various databases in investigative contexts. These concerns highlight the need for transparency, accountability, and clear policy guidance.

To address these challenges, Perrin outlined emerging governance frameworks and best practices. He emphasized the importance of internal oversight mechanisms such as those being developed by the RCMP, Vancouver Police Department, and others, as well as the need for meaningful third-party oversight. Tools like Privacy Impact Assessments, Human Rights AI Impact Assessments, and transparency frameworks such as the RCMP’s National Technology Onboarding Program were highlighted as key components of responsible adoption.

In closing, Perrin stressed the importance of a precautionary, self-governed approach to AI integration in policing, supported by strong accountability measures and cross-sector collaboration. He encouraged agencies to remain focused on maintaining public trust while navigating this complex and rapidly evolving technological landscape.

Professor Benjamin Perrin’s research and case materials can be found at: https://benjaminperrin.ca/ai,