Fair process : an examination of the use of automated decision-making systems in Canadian administrative law through the case study of Canadian immigration.
Bibliographic record
Abstract
As a result of fast-moving developments in artificial intelligence (“AI”) tools such as machine-learning (“ML”), Canadian scholars have been engaged in a recent, concerted effort to examine how the use of automated (often also termed “algorithmic”) decision-making systems (“ADMs”) by public administration officials may alter traditional decision-making processes. This examination has been focused on exploring the impacts these technologies are having on foundational administrative law principles, primarily through the lens of ex post adjudication and judicial review. This thesis continues this line of scholarship by exploring a specific problematic, how the use of ADMs by Immigration, Refugees and Citizenship Canada (“IRCC”), is altering the process of decision-making in a way that necessarily creates implications for the way external mechanisms like judicial review, are able to aid in reviewing decisions. This enquiry is driven through doctrinal method, applying a law and technology approach, and applying Michael Adler’s administrative justice theories and typologies. Tracing how decisions have shifted, I argue that failures to understand process are impacting both procedural fairness and reasonableness review. Furthermore, I argue that the ability to understand and interrogate process, as a prerequisite, requires greater transparency, accountability, and structures of ex ante rulemaking. I conclude with a strong recommendation for structural reform aimed at “getting it right the first time” – suggesting a starting point of procedural protections within statute, namely the Immigration and Refugee Protection Act and developing procedural code, inviting refinement.
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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 | 0.031 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.050 | 0.031 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".