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How AI analysis of police bodycam video can identify best policing practices

Jonathan Wender, Ph.D., discusses the impact of TrustStat AI on police work and community interactions

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Polis Solutions recently announced the commercial release of TrustStat, a multi-modal AI system for the analysis of police body-worn camera (BWC) video. TrustStat integrates three kinds of advanced AI technology to enable police agencies and communities to examine key patterns in interactions between officers and the public. Identifying these patterns empowers agencies and communities with valuable, actionable data on police-community trust and other critical public safety issues.

In today’s episode of the Policing Matters podcast, host Jim Dudley speaks with Jonathan M. Wender, Ph.D., president and co-founder of Polis Solutions, about the benefits body-worn camera analysis can bring to police departments, officers and the communities they serve.

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About our guest

Jonathan M. Wender, PhD., is president and co-founder of Polis Solutions, Inc. He is a 20-year police veteran, interdisciplinary social scientist, and internationally recognized expert on police reform, police decision-making, and the use of force. Jonathan’s area of expertise is face-to-face interactions in situations where risk is high and trust is low. He is the lead developer of Polis’ T3 – Tact, Tactics, and Trust and ADAPT training systems.

Memorable quotes

  • “Right now, the way we look at body-worn camera footage is almost always driven by a negative paradigm. It’s driven by a fear of failure rather than a celebration and analysis of success.”
  • “As you know, for any department, body-worn camera footage is the agency’s single largest source of data about its interactions with the community. It’s a treasure trove, and up to this point, we haven’t had effective, accurate, science-based technology to analyze this critical source of data, which has profound implications for both officer and public safety.”
  • “Artificial intelligence is neither artificial nor intelligent. It represents the collective input of human beings.”
  • “We’re not in the business of telling who’s right or wrong or good or bad. We provide critical information about patterns.”

Key takeaways

  1. AI’s role in policing: TrustStat utilizes AI to analyze large volumes of body camera footage, providing insights into police-community interactions.
  2. Objective analysis: The technology focuses on patterns and dynamics of interactions rather than labeling individuals as “good” or “bad.”
  3. Data-driven insights: TrustStat helps in understanding successful police interactions, contributing to better training and policymaking.
  4. Enhancing police training: The system accelerates the development of police expertise by identifying and teaching best practices.
  5. Community and officer safety: By analyzing effective de-escalation and communication strategies, TrustStat aims to improve safety and trust between officers and the community.

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Dive into the complexities of interpreting body-cam videos accurately in the video below. Learn about the challenges and best practices for ensuring transparency and accountability.

Policing Matters law enforcement podcast with host Jim Dudley features law enforcement and criminal justice experts discussing critical issues in policing