@inproceedings{golazizian-etal-2026-subjectivity,
    title = "The Subjectivity of Respect in Police Traffic Stops: Modeling Community Perspectives in Body-Worn Camera Footage",
    author = "Golazizian, Preni  and
      Rahmati, Elnaz  and
      Trager, Jackson  and
      Sourati, Zhivar  and
      Ghazizadeh, Nona  and
      Chochlakis, Georgios  and
      Alcocer, Jose J.  and
      Bennett, Kerby  and
      Devnani, Aarya Vijay  and
      Hejabi, Parsa  and
      Muttram, Harry G.  and
      Padte, Akshay Kiran  and
      Saadatinia, Mehrshad  and
      Wu, Chenhao  and
      Ziabari, Alireza Salkhordeh  and
      Sierra-Ar{\textbackslash}'evalo, Michael  and
      Weller, Nicholas  and
      Narayanan, Shrikanth  and
      Graham, Benjamin A.t.  and
      Dehghani, Morteza",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1564/",
    pages = "33939--33961",
    ISBN = "979-8-89176-390-6",
    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, (i) we develop a domain-specific evaluation rubric grounded in procedural justice theory, LAPD training materials, and extensive fieldwork; (ii) we introduce a criterion-driven preference data construction framework for perspective-consistent alignment, and (ii) we 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."
}