Administrative Intelligence: Exploring Balanced Human-AI Decision-making Relationships in Canadian Administrative Contexts
Bibliographic record
Abstract
One of the many promising potentials of artificial intelligence (AI) is its ability to improve public decision-making. But, until there is enough confidence for AI systems to act independently, humans will still play a prominent role in making decisions. However, these human-AI decision-making relationships create a new dynamic that, if not properly structured, can end up working against the principles and goals of administrative law. This thesis will focus on the human element in human-AI decision-making relationships and how these dynamics can be structured to promote the goals and principles of public decision-making. From the perspective of Canadian administrative law, the focus of this thesis will be a qualitative case study on three prominent AI decision-making systems – COMPAS, iBorderCtrl, and PredPol – that are already implemented in jurisdictions with analogous administrative law requirements.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".