Automation and Artificial Intelligence in Police Body-Worn Cameras: Experimental Evidence of Impact on Perceptions of Fairness Among Officers

Abstract

Objectives: Explore officers' perceptions of the fairness of monitoring with systematic variations in activation (manual/automatic) and auditing (on-demand/supervisor random/artificial intelligence) policy regimes for body-worn cameras (BWCs). Methods: This study uses a survey experiment with a national probability sample of officers wearing BWCs (n=258) to assess the perceived fairness of BWC monitoring under varying activation and audit policies. Participants were randomly assigned one of six vignettes, each incorporating one of two BWC activation policies and one of three BWC footage review policies. The analysis involves a 2x3 experimental design to assess main and interaction effects. Results: Automatic BWC activation and artificial intelligence enabled auditing of footage cause declines in perceived fairness of monitoring. Officers perceive the most unfairness in monitoring when they lack control over the initiation of recording and when the resultant footage is outside of their supervisors' immediate control. Conclusions: The findings underscore potential adverse effects on officers' perceptions of monitoring fairness under varying BWC policy conditions. As this technology gains traction, the potential impact of officers’ concerns on program implementation and fidelity should be considered.

Publication
CrimRxiv

Summary

A new study found that police officers view body camera monitoring as less fair when cameras automatically turn on and when artificial intelligence reviews the footage, rather than having manual control and human supervisors doing the reviews. Officers feel most uncomfortable when they can’t control when recording starts and when the footage review process is taken out of their direct supervisors' hands. These findings matter because officer resistance to automated body camera systems could undermine their effectiveness in improving police accountability and transparency.

(AI-generated summary, v1, January 2026)

Citation Information

Citations: 2 (as of January 2026)

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