Algorithmic Racism: De-coding Bias in A.I. Facial Recognition Technology

Facial recognition technology (FRT) is increasingly deployed across both public and private sectors – from policing, immigration, and administrative enforcement to loss-prevention in the retail industry – under claims of efficiency, objectivity, and technological neutrality. This presentation challenges those claims by advancing the concept of algorithmic racism: the structural embedding of racial hierarchy into ostensibly neutral algorithmic systems through biased data, design choices, institutional incentives, and opaque deployment practices. Drawing on documented wrongful arrests in the United States, Federal Court litigation in Canada involving refugee-status revocations, and the growing use of FRT in commercial settings, the presentation demonstrates how FRT disproportionately misidentifies Black individuals, particularly Black women.

The presentation situates the well-documented racial bias in FRT within a longer historical arc, arguing that contemporary FRT mirrors the exclusionary logic of the Jim Crow era – reproducing systemic racial hierarchy through ostensibly neutral technological systems. Framed as a modern continuation of past forms of racialized control, FRT operates not through explicit racial classification but through code, data, and automated decision-making. The presentation contends that, absent robust legal safeguards, transparency, and meaningful accountability, the deployment of facial recognition technologies risks entrenching algorithmic racism across both state and market institutions, underscoring the need for urgent regulatory intervention.

Gideon Christian
https://profiles.ucalgary.ca/gideon-christian
Associate Professor
Faculty of Law
University of Calgary

About the Speaker:

My primary research interest is specifically in the areas of artificial intelligence (AI) and law, and generally in law and technology. My research work sheds light on the intersection of AI and law, from identifying and addressing legal issues arising from the adoption of new and emerging technologies in the justice system to identifying and addressing issues relating to racial bias in AI systems.

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This is a talk with audience Q&A presented by the University of Toronto's Centre for Ethics that is free to attend and open to the public. The talk will also be streamed online with live chat here [to be posted].

About the Centre for Ethics (http://ethics.utoronto.ca):

The Centre for Ethics is an interdisciplinary centre aimed at advancing research and teaching in the field of ethics, broadly defined. The Centre seeks to bring together the theoretical and practical knowledge of diverse scholars, students, public servants and social leaders in order to increase understanding of the ethical dimensions of individual, social, and political life.

In pursuit of its interdisciplinary mission, the Centre fosters lines of inquiry such as (1) foundations of ethics, which encompasses the history of ethics and core concepts in the philosophical study of ethics; (2) ethics in action, which relates theory to practice in key domains of social life, including bioethics, business ethics, and ethics in the public sphere; and (3) ethics in translation, which draws upon the rich multiculturalism of the City of Toronto and addresses the ethics of multicultural societies, ethical discourse across religious and cultural boundaries, and the ethics of international society.

The Ethics of A.I. Lab at the Centre For Ethics recently appeared on a list of 10 organizations leading the way in ethical A.I.: https://ocean.sagepub.com/blog/10-organizations-leading-the-way-in-ethical-ai