Rebalancing Rights: how Generative AI forces a rethink of fair use/ fair dealing under Canadian and USA law
Bibliographic record
Abstract
The rapid emergence of generative artificial intelligence has unsettled longstanding assumptions in copyright law, challenging both the United States’ flexible fair use doctrine and Canada’s more structured fair dealing framework. As AI systems rely on large-scale ingestion and reproduction of copyrighted material for model training, they expose doctrinal gaps in how each jurisdiction conceptualizes reproduction, transformation, substantiality, and user rights. This article examines how generative AI disrupts traditional copyright norms by blurring the boundaries between analytical computation and derivative expression. Through a comparative analysis of U.S. and Canadian law, it highlights the contrasting flexibility of fair use and the purpose-based constraints of fair dealing, showing how both regimes struggle to accommodate large-scale machine learning processes. The article argues that neither framework designed for human-centered creativity adequately addresses the economic, moral, and control interests of creators in the AI era. It proposes a recalibration of copyright exceptions and recommends the adoption of a statutory AI Training Exception incorporating transparency, auditing, and compensation mechanisms. Such reform, it contends, is essential to rebalancing the rights of creators, users, and innovators while preserving technological progress and legal certainty in the age of generative AI.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.025 | 0.066 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".