Is discrimination in artificial intelligence sufficiently regulated?
Bibliographic record
Abstract
Artificial intelligence (AI), and more specifically machine learning, offers new opportunities in several fundamentally different fields (e.g. health, finance, law, construction). This technology is becoming increasingly important in our society and is revolutionizing how we process and use data. However, AI replicates human cognitive biases, some of which can be discriminatory. As a result, its use by the private sector should not be at the expense of the right to equality and privacy. In this essay, we focus on the Canadian and, more specifically, the Quebec legislative framework for the private sector regarding discrimination through the use of AI. We argue that there are gaps in ensuring an effective framework to reduce the risk of discriminative bias (discrimination) when developing AI solutions. We begin our analysis of this framing by taking care to introduce the key concepts to our analysis (i.e. machine learning, cognitive biases, discriminatory biases, and ethical issues). Next, we will present some cases where the use of AI by the private sector has been a failure in terms of discrimination and ethics. We then study the various national and international initiatives to frame AI practices (e.g. the Montreal Declaration). Finally, we propose possible solutions to improve the current Quebec framework to regulate AI practices concerning discrimination. We propose, among other things, to modify the responsibilities and powers of the Commission des droits de la personne et des droits de la jeunesse and to add new responsibilities for private companies.
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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 | 0.043 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.087 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".