Bots, Bias, and Borders: The effects of automated decision making on Canadian immigration systems
Bibliographic record
Abstract
When used in public programs, AI-based technologies, whether through automated decision-making systems or through surveillance software (i.e., facial recognition), act as sieving tools for the state by extracting, sorting, and creating social categories of the “good” subject and the “bad” or even non-subject. The convergence of AI-based decision-making and algorithmic governance with new and emerging immigration technologies enacts and furthers racial statecraft. Questions of race and racialization have become even more pronounced as states seek to pre-empt risk through “smart” border technologies in the post 9/11 era. Given that discourses around refugees and asylum-seeking cannot be divorced from projects of sovereignty, the deployment of AI-powered border technologies must be studied in relation to the enforcement of sovereignty through technological bordering regimes and global geopolitical hierarchies Within Canada, the deployment of AI tools in border technologies is implicated along several registers: neocolonial geo-political asymmetries of power; the low rights environments for displaced persons; and statelessness, which has become both a basis for data extraction and the means to mitigate the mobility of asylum and refugee claimants. This dissertation argues that the nature and operations of AI systems in the context of Canadian immigration policy and practice both shapes and is shaped by social categories of difference such as race, gender, and citizenship status. The proliferation and use of AI in Canadian immigration signifies a mode of statecraft through the consolidation of the state’s AI capabilities and power as expressed through innovation in and desire for a strong domestic AI economy and market. Finally, it argues that Canada’s use of AI and digitally mediated decision making further exacerbates ongoing issues of racialized and gendered discrimination, further prompting the need for social innovation over technosolutionism.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".