Protecting People with Disabilities: A Guide for Non-Technical Committee Members in Understanding the Regulations Needed to Design Ethical AI
Bibliographic record
Abstract
Artificial Intelligence (AI) promises large-scale efficiencies that enable faster and “better” decisions. What was once a tool for researchers and technologists has now been made accessible to corporations, regulators, and individuals. Through its rate of development and increased adoption, AI systems and tools are being used to replace human decision-making at a speed that surpasses regulation and intervention. The speed of mass AI adoption and lack of regulation towards protecting communities most impacted by the technology. This is resulting in statistical discrimination and cumulative harm against the most vulnerable groups in society, people with disabilities. \n \n \n \nTo bring attention to the statistical discrimination and cumulative against people with disabilities, this design and research project contributes to the work of the Capacity Building Seed group and their efforts in standardizing and publishing equitable AI regulations as part of the Accessible Standards Canada priorities. This design and research project contributes to bridging the technical and legal gaps for non-technical committee members that require this information to make informed decisions about the proposed clauses. The outcome of this design and research project is a capacity building resource, which supports a larger working group who developed the Seed Standards, which are proposed regulatory standards for equitable AI regulations that protect people with disabilities in efforts to prevent further harm. \n \n \n \nKeywords: Artificial Intelligence, AI Regulation, Trustworthy AI, People with Disabilities, Statistical Discrimination, Data Outlier, Cumulative Harm.
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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 | 0.049 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.021 | 0.026 |
| Insufficient payload (model declined to judge) | 0.028 | 0.048 |
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".