Conference session

This session examines a pressing contemporary question: what happens to public trust, democratic legitimacy, and good governance when policing increasingly relies on facial recognition technologies and other forms of algorithmic authority built on historically racialized data? Drawing on doctoral research on policing, oversight, and Black community experiences in Canada, the presentation argues that facial recognition is not merely a technical instrument of identification but a socially mediated interpretive practice that can reproduce anti-Black surveillance under the guise of computational neutrality. The session will benefit scholars, policymakers, community advocates, educators, students, and justice-sector practitioners interested in artificial intelligence, public accountability, race, surveillance, and democratic renewal. It will be especially valuable to those seeking to understand how humanities and social sciences perspectives can intervene meaningfully in debates often dominated by technical claims of efficiency and objectivity. Participants will leave with three key insights. First, they will gain a critical framework for understanding how “dirty data” and algorithmic policing convert historical inequities into automated forms of suspicion. Second, they will see why trust cannot be rebuilt through technological optimization alone, but requires interpretive accountability, ethical transparency, and community-informed oversight. Third, they will encounter practical pathways through which HSS scholarship can help reshape AI governance, including ethnographically grounded evaluation, participatory oversight, and more context-sensitive approaches to justice, legitimacy, and institutional responsibility.