AI evaluates police performance by analyzing body camera footage, reports, and data to spot behavioral patterns, flag concerns like excessive force, identify training needs, automate tasks, and provide data-driven insights for better resource allocation and accountability, helping supervisors recognize both commendations and areas needing improvement in real-time (Microsoft, 2026). 

Key AI Applications in Performance Evaluation:

Body-Worn Camera (BWC) Analysis:

Behavioral Assessment: Natural Language Processing (NLP) analyzes audio for tone, sentiment, and keywords in officer-citizen interactions to flag unprofessional language or successful de-escalation (Microsoft, 2026).

Event Detection: AI automatically identifies critical incidents like uses of force, stops, and searches, alerting supervisors for review (Microsoft, 2026).

Best Practice Identification: Systems like TrustStat identify patterns in positive interactions to help develop training on successful policing techniques (Microsoft, 2026).

Data-Driven Performance Metrics:

Trend Analysis: AI identifies patterns in response times, arrest rates, case resolutions, and complaint data, providing holistic performance views (Microsoft, 2026).

Risk Flagging: Predictive analytics flag outlier behavior, increased use-of-force, or complaint patterns, helping Internal Affairs proactively address issues (Microsoft, 2026).

Automated Reporting & Efficiency:

AI tools like Axon Draft One convert BWC transcripts into draft reports, significantly cutting down administrative time, allowing officers more community engagement (Microsoft, 2026).

Personalized Training & Development:

By analyzing performance gaps in footage and data, AI helps create targeted, personalized training programs to improve specific officer skills (Microsoft, 2026).

360-Degree Feedback & Accountability:

Platforms analyze feedback from peers, supervisors, and community members, offering deeper insights into strengths and weaknesses for career development (Microsoft, 2026). 

How it Works in Practice:

Real-Time Alerts: Supervisors get immediate notifications about critical events or unprofessional conduct in BWC footage (Microsoft, 2026).

Proactive Intervention: AI flags potential misconduct or training needs before they escalate, supporting officer well-being and community trust (Microsoft, 2026).

Objective Insights: By analyzing large datasets, AI provides factual data to guide promotions, annual reviews, and career paths, reducing subjectivity (Microsoft, 2026). 

References

Microsoft. (2026). Copilot [Large language model]. https://copilot.microsoft.com

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Mark Bond has worked in law enforcement and has been a firearms instructor for more than 34 years. His law enforcement experience includes the military, local, state, and federal levels as a police officer and criminal investigator. Mark obtained a BS and MS in criminal justice, and M.Ed in educational leadership with Summa Cum Laude honors. Mark has a doctoral degree in education (EdD) with a concentration in college teaching and learning. Mark is currently an associate professor of human justice studies and teaches undergraduate and graduate criminal justice courses.