The New Jim Crow: Unmasking Racial Bias in AI Facial Recognition Technology within the Canadian Immigration System - Christian, Gideon (McGill Law Journal / Revue de droit de McGill)

(untitled) (McGill Law Journal / Revue de droit de McGill, 2024)
Despite its purported neutrality, AI-based facial recognition technology (FRT) exhibits significant racial bias. This paper critically examines the integration of FRT within the Canadian immigration system. The paper begins with an exploration of the historical evolution of AI in border control—once rooted in physical barriers—which now relies on biometric surveillance that risks replicating historical patterns of racial discrimination.The paper further contextualizes these issues within the broader discourse of algorithmic racism, highlighting the risks of embedding historical racial injustices into AI-powered immigration systems. Drawing a parallel between FRT and Jim Crow laws that segregated and marginalized Black communities in the United States, it argues that biased FRT systems function as a modern mechanism of racial exclusion, risk denying Black and racialized immigrants access to refugee protection, and exacerbating deportation risks. It warns against the normalization of AI use in immigration decision-making without proper oversight, transparency, and regulatory safeguards.The paper concludes by calling for enhanced government transparency and adherence to procedural fairness in the deployment of FRT within the Canadian immigration system. It further advocates for a “technological civil rights movement” to ensure that AI technologies, including FRT, uphold human rights and promote equity rather than perpetuate systemic racism.