Bibliographic record
Abstract
While Canada is revered as a migration hub, discriminatory practices remain a persistent issue at Canada’s borders. In 2017, the RCMP was publicly condemned for distributing Islamophobic, religiously coded questionnaires amongst Muslim migrants on the border between Quebec and the United States. Since 2014, the Canadian government has deployed artificial intelligence-led decision making in Canada’s immigration system. (Molnar, 2018) The use of these technologies has an alarming impact on the internationally recognised human rights, and s15, s2, s8 and s6 of the Canadian Charter of Rights and Freedoms. (Molnar and Gil, 2018) In this essay, I will argue that the use of predictive analysis and automated decision-making systems in Canadia’s immigration decisions can lead to serious breaches of the right to privacy, the right to movement, the right to freedom of association and the right to be free from discrimination. In the first half of this paper, I will define predictive analysis and automated decision-making systems, and outline how AI is used in immigration in the Canadian context. Then, I will discuss the human rights protections violated through automated decision systems in the Canadian immigration system.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".