  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.
