The Subjectivity of Respect in Police Traffic Stops: Modeling Community Perspectives in Body-Worn Camera Footage
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2026
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| _version_ | 1866912912649486336 |
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| author | Golazizian, Preni Rahmati, Elnaz Trager, Jackson Sourati, Zhivar Ghazizadeh, Nona Chochlakis, Georgios Alcocer, Jose Bennett, Kerby Devnani, Aarya Vijay Hejabi, Parsa Muttram, Harry G. Padte, Akshay Kiran Saadatinia, Mehrshad Wu, Chenhao Ziabari, Alireza S. Sierra-Arévalo, Michael Weller, Nick Narayanan, Shrikanth Graham, Benjamin A. T. Dehghani, Morteza |
| author_facet | Golazizian, Preni Rahmati, Elnaz Trager, Jackson Sourati, Zhivar Ghazizadeh, Nona Chochlakis, Georgios Alcocer, Jose Bennett, Kerby Devnani, Aarya Vijay Hejabi, Parsa Muttram, Harry G. Padte, Akshay Kiran Saadatinia, Mehrshad Wu, Chenhao Ziabari, Alireza S. Sierra-Arévalo, Michael Weller, Nick Narayanan, Shrikanth Graham, Benjamin A. T. Dehghani, Morteza |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_10339 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Subjectivity of Respect in Police Traffic Stops: Modeling Community Perspectives in Body-Worn Camera Footage Golazizian, Preni Rahmati, Elnaz Trager, Jackson Sourati, Zhivar Ghazizadeh, Nona Chochlakis, Georgios Alcocer, Jose Bennett, Kerby Devnani, Aarya Vijay Hejabi, Parsa Muttram, Harry G. Padte, Akshay Kiran Saadatinia, Mehrshad Wu, Chenhao Ziabari, Alireza S. Sierra-Arévalo, Michael Weller, Nick Narayanan, Shrikanth Graham, Benjamin A. T. Dehghani, Morteza Computation and Language 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. |
| title | The Subjectivity of Respect in Police Traffic Stops: Modeling Community Perspectives in Body-Worn Camera Footage |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2602.10339 |