Standards for the control of algorithmic bias in the Canadian administrative context
Bibliographic record
Abstract
Governments around the world use machine learning in automated decision-making systems for a broad range of functions, including the administration and delivery of healthcare services, education, housing benefits; for surveillance; and, within policing and criminal justice systems. Algorithmic bias in machine learning can result in automated decisions that produce disparate impact, compromising Charter guarantees of substantive equality. The regulatory landscape for automated decision-making, in Canada and across the world, is far from settled. Legislative and policy models are emerging, and the role of standards is evolving to support regulatory objectives. This thesis seeks to answer the question: what standards should be applied to machine learning to mitigate disparate impact in automated decision-making? While acknowledging the contributions of leading standards development organizations, I argue that the rationale for standards must come from the law, and that implementing such standards would help not only to reduce future complaints, but more importantly would proactively enable human rights protections for those subject to automated decision-making. Drawing from the principles of administrative law, and the Supreme Court of Canada’s substantive equality decision in Fraser v. Canada (Attorney General), this research derives a proposed standards framework that includes: standards to mitigate the creation of biased predictions; standards for the evaluation of predictions; and, standards for the measurement of disparity in predictions. Recommendations are provided for implementing the proposed standards framework in the context of Canada’s Directive on Automated Decision-Making.
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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 | 0.094 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.023 | 0.030 |
| Scholarly communication | 0.026 | 0.009 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".