Artificial Intelligence in Canadian Healthcare: Will the Law Protect Us from Algorithmic Bias Resulting in Discrimination?
Bibliographic record
Abstract
In this article, we canvas why AI may perpetuate or exacerbate extant discrimination through a review of the training, development, and implementation of healthcare-related AI applications and set out policy options to militate against such discrimination. The article is divided into eight short parts including this introduction. Part II focuses on explaining AI, some of its basic functions and processes, and its relevance to healthcare. In Part III, we define and explain the difference and relationship between algorithmic bias and data bias, both of which can result in discrimination in healthcare settings, and provide some prominent examples of healthcare-related AI applications that have resulted in discrimination or have produced discriminatory outputs. Part IV explains in more detail differences between algorithmic bias and data bias, with a focus on data bias and data governance, including the non-representativeness of data sets used in training AI. From this point we turn to look at possible legal responses to the problem of algorithmic discrimination, and, in Part V, we demonstrate the insufficiency of existing ex post legal protections (i.e., legal protections that offer redress after someone has suffered harm), including claims in negligence, under human rights legislation, and under the Charter of Rights and Freedoms. Part VI explores possibilities within the Canadian ex ante legal landscape (i.e., the regulation of AI applications before they become available for use in healthcare settings), notably through federal regulation of medical devices, and identifies gaps in oversight. Finally, in Part VII we provide recommendations for federal and provincial governments and innovators as to the appropriate governance and regulatory approach to counter algorithmic and data bias that results in discrimination in healthcare-related AI, before concluding in Part VIII.
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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 | 0.028 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.015 | 0.036 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".