[2402.01703] Community-Informed AI Models for Police Accountability

Computer Science > Computers and Society arXiv:2402.01703 (cs) [Submitted on 24 Jan 2024 (v1), last revised 20 Mar 2026 (this version, v5)] Title:Community-Informed AI Models for Police Accountability Authors:Benjamin A.T. Grahama, Lauren Brown, Georgios Chochlakis, Morteza Dehghani, Raquel Delerme, Brittany Friedman, Ellie Graeden, Preni Golazizian, Rajat Hebbar, Parsa Hejabi, Aditya Kommineni, Mayagüez Salinas, Michael Sierra-Arévalo, Jackson Trager, Nicholas Weller, Shrikanth Narayanan View a PDF of the paper titled Community-Informed AI Models for Police Accountability, by Benjamin A.T. Grahama and 15 other authors View PDF Abstract:Face-to-face interactions between police officers and the public affect both individual well-being and democratic legitimacy. Many government-public interactions are captured on video, including interactions between police officers and drivers captured on bodyworn cameras (BWCs). New advances in AI technology enable these interactions to be analyzed at scale, opening promising avenues for improving government transparency and accountability. However, for AI to serve democratic governance effectively, models must be designed to include the preferences and perspectives of the governed. This article proposes a community-informed, approach to developing multi-perspective AI tools for government accountability. We illustrate our approach by describing the research project through which the approach was inductively developed: an effort to build A...