The Subjectivity of Respect in Police Traffic Stops: Modeling Community Perspectives in Body-Worn Camera Footage

2026-02-10

AI Summary

This study addresses the subjective nature of “respect” in police traffic stops, which varies significantly across community groups and critically influences perceptions of law enforcement legitimacy and public trust. Leveraging body-worn camera footage from the Los Angeles Police Department, the authors construct the first large-scale, multi-perspective annotated dataset on respect, incorporating evaluations and justifications from three distinct groups: individuals with police contact, those affected by the justice system, and residents unaffiliated with either. Guided by procedural justice theory, they propose a perspective-aware modeling framework that enables personalized prediction of respect scores along with interpretable explanations. Experimental results demonstrate that the approach substantially improves both prediction accuracy and the coherence of generated rationales across all three perspectives, offering law enforcement agencies a valuable tool to better understand diverse community expectations and enhance public trust.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Security and Privacy: Data transparency and provenance

Abstract

Traffic stops are among the most frequent police-civilian interactions, and body-worn cameras (BWCs) provide a unique record of how these encounters unfold. Respect is a central dimension of these interactions, shaping public trust and perceived legitimacy, yet its interpretation is inherently subjective and shaped by lived experience, rendering community-specific perspectives a critical consideration. Leveraging unprecedented access to Los Angeles Police Department BWC footage, we introduce the first large-scale traffic-stop dataset annotated with respect ratings and free-text rationales from multiple perspectives. By sampling annotators from police-affiliated, justice-system-impacted, and non-affiliated Los Angeles residents, we enable the systematic study of perceptual differences across diverse communities. To this end, we (i) develop a domain-specific evaluation rubric grounded in procedural justice theory, LAPD training materials, and extensive fieldwork; (ii) introduce a rubric-driven preference data construction framework for perspective-consistent alignment; and (iii) propose a perspective-aware modeling framework that predicts personalized respect ratings and generates annotator-specific rationales for both officers and civilian drivers from traffic-stop transcripts. Across all three annotator groups, our approach improves both rating prediction performance and rationale alignment. Our perspective-aware framework enables law enforcement to better understand diverse community expectations, providing a vital tool for building public trust and procedural legitimacy.

Problem

• respect
• police traffic stops
• subjectivity
• community perspectives
• body-worn camera

Innovation

• perspective-aware modeling
• respect prediction
• body-worn camera footage
• procedural justice
• preference alignment