Artificial impartiality: the insidious role of AI in reinforcing white supremacy in Canada
Bibliographic record
Abstract
Using a critical race lens, this paper explores how artificial intelligence (AI) oppresses racialized individuals while reinforcing white supremacy. This issue is important given Canada’s investment and use of AI technologies in multiple sectors, including law enforcement, immigration, and employment — the use of which has led to systemic discrimination against racialized groups and violations of their fundamental human rights. By exploring how AI is characterized as White, the use of AI in eliminating the fuller attributes of racialized bodies, and the racially homogenous nature of the tech sector, this paper demonstrates that AI advances white supremacist ideology by creating a system where White individuals overwhelmingly control power and privilege. In addition, magnifying the lived experiences of racialized individuals reveals how AI systemically discriminates against racialized groups, further oppressing racialized individuals. This discrimination also infringes on fundamental human rights, as this paper examines in international and domestic law. It is clear that AI governance is required to combat the discriminatory impacts of AI. To do so, this paper advocates for AI governance based on a human rights approach and has provided recommendations to mitigate the discriminatory impact of AI.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.039 | 0.020 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".