Analysis of Artificial Intelligence and Data Act based on ethical frameworks
Bibliographic record
Abstract
Canada has recently introduced the Digital Charter Implementation Act of 2022. Both at the federal level as well as at the provincial level, different governments are trying to move forward with emerging technology by amending and implementing laws and regulations to put a safety net against various emerging risks, but at the same time, promote the growth of the innovative industry in Canada. Indeed, as one of the first countries, Canada introduced legislation regarding artificial intelligence (Artificial Intelligence and Data Act), and the goal of this paper is to check if the new legislation would encompass the basic artificial intelligence ethical frameworks such as privacy, accountability, transparency/explainability, fairness, and safety & security, which were recommended in three (3) different ethical declarations on artificial intelligence, among many others. The Artificial Intelligence and Data Act contains important sections incorporating accountability, transparency/explainability, fairness, and safety & security. Considering a risk-based approach targets high-impact systems and requires risk management and monitoring against the risk of harm and biased output. It also requires persons responsible for high-impact systems to communicate all the necessary information publicly and to be audited in case of concern with its artificial intelligence systems. The new legislation imposes administrative monetary penalties and offenses similar to the proposed regulations in the EU. As for privacy, similar to the EU, it is outside of the new law's scope, but another law covers the topic independently already. Nevertheless, the Artificial Intelligence and Data Act also has some pitfalls. It lacks clarity and specific requirements found in the law of the EU. Furthermore, the scope is an issue as it only covers private sector actors, and there is doubt about the real independence and neutrality of the commissioner.
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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 | 0.050 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".