The Ethics of Surveillance AI: Framing Data as a Socio-collective Good in Mitigating Data Colonialism

Abstract

In my thesis, I examine how facial recognition technology (FRT) in the Global South operates within a system of data colonialism, where powerful organizations extract and exploit data from marginalized populations without meaningful consent, oversight, or benefit to those being surveilled. I argue that the ethical failure of data collection, storage, and usage in the context of FRT stems from a deeper conceptual failure: data is wrongly framed as capital rather than as a socio-collective good. Framing data is not a neutral or technical choice—it reflects how we understand identity, power, and social relations. Treating data as capital enables extractive and coercive practices that undermine dignity, autonomy, and justice, especially in contexts where communities lack the institutional means to challenge how their data is used. By contrast, reframing data as a socio-collective good—embedded in community, shaped by social relations, and subject to collective governance—exposes the moral structure of FRT deployment and clarifies the ethical duties owed to those whose data is captured, stored, and used. This framing compels a shift from individual consent and technical accuracy toward relational autonomy, contextual integrity, and shared accountability.