Policing with Augmented Reality

Abstract

This case study explores the ethical, legal, and societal implications of using Augmented Reality (AR) and facial recognition in law enforcement, focusing on the fictional arrest of Terence Macknamy, an African American man flagged as a “probable offender” by AR-assisted technology. The ARALE system integrates facial recognition, behavioral analysis, and personal data to classify individuals in real-time, perpetuating racially biased outcomes under the guise of objectivity. Despite arguments that AR reduces human bias, evidence shows that emotion-recognition AI and facial identification systems perform less accurately on people of color, reinforcing systemic inequalities. The case parallels current and historical concerns with racial profiling, “stop and frisk” practices, and the unequal burden of surveillance technologies. The lawsuit against the Provo Police Department highlights the challenge of proving discriminatory “intent” under the Equal Protection Clause and raises deeper questions about algorithmic justice, data ethics, and panoptic surveillance. As AR technologies become more ubiquitous, the case calls for critical reflection on the trade-offs between public safety, privacy, and racial justice in the age of predictive policing.