The Challenges of Accountability in AI for Immigration: The IRCC and Canadian AI Governance
Bibliographic record
Abstract
The use of AI in the IRCC continues to be a topic of debate, especially regarding ethical concerns and the potential for harm. While AI has been used to streamline processes, several of its uses by the IRCC have raised concerns. The key characteristics of IRCC applicants are location, age and gender. Each comes with its own set of issues. Even when attempting to use debiasing tools, developers risk “removing characteristics that could be important for decisions like refugee determinations.” This can cause concerns, especially when AI is used in departments or agencies that experience high public scrutiny, such as the IRCC. Another concerning precedent is the use of facial recognition technology (FRT) findings as evidence in immigration and refugee hearings, where the burden of proof is critical. This case study discusses the governance dilemma about whether to continue with systems that “get the job done” but are opaque and pose a risk to institutional integrity and obligation, or to integrate more transparent processes and human rights safeguards that could make the implementation of some AI tools more difficult.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.038 | 0.020 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".