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 AI tools to analyze BWC footage of traffic stops conducted by the Los Angeles Police Department. We focus on the role of social scientists as members of multidisciplinary teams responsible for integrating the perspectives of diverse stakeholders into the development of AI tools in the domain of police -- and government -- accountability.

翻译：警察与公众之间的面对面互动既影响个人福祉，也影响民主合法性。许多政府与公众的互动都被视频记录，包括警察与司机之间的互动，这些互动由随身摄像头（BWC）捕捉。AI技术的新进展使得这些互动得以大规模分析，为提高政府透明度和问责制开辟了有前景的途径。然而，为了使AI有效服务于民主治理，模型的设计必须包含被治理者的偏好和视角。本文提出了一种社区参与的方法，用于开发面向政府问责的多视角AI工具。我们通过描述该方法的归纳式开发过程来阐明这一方法：即构建AI工具以分析洛杉矶警察局交通拦截中的BWC录像。我们重点关注社会科学家作为多学科团队成员的角色，他们负责将不同利益相关者的视角整合到警察及政府问责领域的AI工具开发中。