ARTIFICIAL INTELLIGENCE AS IM/MOBILITY: PRE-LIMIRARY THOUGHTS ON UNDERSTANDING THE USE OF AI IN IMMIGRATION SYSTEMS
Bibliographic record
Abstract
Technology, including artificial intelligence in im/migration service delivery and migration management have been widely deployed in EuroAmerica (Mongia, 2018; Walia, 2021). In Canada, Immigration Refugee Citizenship Canada (IRCC) has, in the past few years, secretly piloted an in-house built AI system for triaging im/migrant applications from China, India and the Philippines (Molnar & Gill, 2018). The 2 million immigration applications backlog in part caused by the COVID-19 pandemic has been used as a justification to accelerate the push for the adoption of AI in im/migration affairs stating the need to modernize, optimize and expedite immigration affairs. Building on AoIR 2022’s theme, I take a decolonial approach to the study of AI and im/migration issues. I follow the claim made by decolonial scholars (El-Nany, 2020) that it is necessary to go back in history in order to better understand current power relations, systemic racism and patriarchy, the ongoing dispossession and forced im/mobility of racialized populations located in the global South. I conceptualize the use of AI as immobility to show how populations become testing grounds to decide who is worthy of im/mobility. Methodologically, this conference paper is informed by access to information requests made to the government of Canada to shed light on the use of AI systems within the IRCC. In addition, this paper relies on desk research including newspaper articles, blog posts and podcasts by and with immigration lawyers, and parliamentary hearings.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.065 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".