ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2402.01703
94
3
v1v2v3 (latest)

A Multi-Perspective Machine Learning Approach to Evaluate Police-Driver Interaction in Los Angeles

24 January 2024
Benjamin A.T. Grahama
Lauren Brown
Georgios Chochlakis
Morteza Dehghani
Raquel Delerme
Brittany Friedman
Ellie Graeden
Preni Golazizian
Rajat Hebbar
Parsa Hejabi
Aditya Kommeneni
Mayaguez Salinas
Michael Sierra-Arévalo
Jackson Trager
Nicholas Weller
Shrikanth Narayan
    HAI
ArXiv (abs)PDFHTML
Abstract

Interactions between the government officials and civilians affect public wellbeing and the state legitimacy that is necessary for the functioning of democratic society. Police officers, the most visible and contacted agents of the state, interact with the public more than 20 million times a year during traffic stops. Today, these interactions are regularly recorded by body-worn cameras (BWCs), which are lauded as a means to enhance police accountability and improve police-public interactions. However, the timely analysis of these recordings is hampered by a lack of reliable automated tools that can enable the analysis of these complex and contested police-public interactions. This article proposes an approach to developing new multi-perspective, multimodal machine learning (ML) tools to analyze the audio, video, and transcript information from this BWC footage. Our approach begins by identifying the aspects of communication most salient to different stakeholders, including both community members and police officers. We move away from modeling approaches built around the existence of a single ground truth and instead utilize new advances in soft labeling to incorporate variation in how different observers perceive the same interactions. We argue that this inclusive approach to the conceptualization and design of new ML tools is broadly applicable to the study of communication and development of analytic tools across domains of human interaction, including education, medicine, and the workplace.

View on arXiv
Comments on this paper