The Ethics of Surveillance AI: Framing Data as a Socio-collective Good in Mitigating Data Colonialism
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.063 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.102 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".