Bibliographic record
Abstract
This article considers whether a decision made by generative artificial intelligence can satisfy the standard of reasonableness set out in Canada (Minister of Citizenship and Immigration) v. Vavilov. Vavilov requires that administrative decisions be justified through reasons that are transparent and intelligible to the affected party. Earlier scholarship, law, and policy have assumed that AI cannot do this because it cannot provide reasons and its inner workings are opaque or uninterpretable. However, new capabilities of large language models challenge this view. Recent experiments show that when prompted with party submissions and relevant legal materials, generative AI can produce persuasive, legally grounded reasons for decisions. The article evaluates two responses: one argues that AI decisions remain unreasonable under Vavilov since their true basis lies in opaque technical processes; the other contends that Vavilov focuses on the cogency of stated reasons, not how they were generated. The article supports the latter position, suggesting that Vavilov leaves open the possibility that AI-generated decisions can be reasonable, provided their reasons meet the decision-making standard applied to human actors.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| 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".