Surveillance Technology and Racialized Policing: How Data-Driven Policing Reinforces Racial Disparities in the U.S

• In Event: 20220 - Section on Communication, Information Technology, and Media Sociology Roundtables
  In Refereed Roundtable (60 minute): Section on Communication, Information Technology, and Media Sociology Roundtable III: Digital Infrastructure I

Abstract

This article examines the role of surveillance technologies in perpetuating racial inequalities within American policing. While these tools—such as predictive policing algorithms, facial recognition systems, and body-worn cameras—are marketed as instruments to enhance fairness and efficiency, their implementation often reinforces systemic racial biases. Drawing from a sociological framework, particularly Critical Race Theory, the article explores how these technologies rely on historically biased data and disproportionately target communities of color. Predictive policing systems perpetuate over-policing in minority neighborhoods, while facial recognition technologies exhibit higher error rates for Black and Latino individuals, leading to misidentifications and reinforcing racial profiling. Furthermore, body-worn cameras, though designed to promote accountability, often validate law enforcement narratives while failing to address systemic disparities. The psychological, social, and economic consequences of these technologies are profound, contributing to mistrust, alienation, and limited mobility in affected communities. This article shows the need for policy interventions, including bias audits, community oversight, and algorithmic transparency, to challenge these inequalities. Ultimately, it calls for a reevaluation of surveillance technologies within a framework of racial equity and civil rights to ensure that they serve justice rather than perpetuate oppression.

Author

• Oluwatimileyin Aborisade, Case Western Reserve University