Bibliographic record
Abstract
This chapter examines the most pertinent issues facing copyright law as it encounters increasingly sophisticated artificial intelligence (AI). It begins with a few introductory examples to illuminate the potential interactions of AI and copyright law. Section 2 then tackles the question of whether AI-generated works are copyrightable in Canada and who, if anyone, might own that copyright. This involves a doctrinal discussion of “originality” (the threshold for copyrightability) as well as reflections on the meaning of “authorship,” and concludes with the suggestion that autonomously generated AI outputs presently (and rightly) belong in the public domain. Section 3 turns to consider issues of copyright infringement. First, it addresses the law in respect of AI inputs (the texts and data used to train AI systems, which may themselves be copyrightable works) and highlights the need for greater limits and exceptions to ensure that copyright law does not obstruct best practices in the development and implementation of AI technologies. It then examines the matter of potentially infringing AI outputs (which may, of course, resemble copyright-protected, human-created works), identifying current uncertainties around independent creation, agency, and the allocation of liability. Section 4 addresses the deployment of AI in automated copyright-enforcement, emphasizing its increasingly critical role in shaping our online environment and citizens’ everyday encounters with copyright enclosures. The chapter concludes with reflections on the risks and opportunities presented by AI in the copyright context, and identifies key gaps and questions that remain to be answered as copyright law and policy adjust to evolving AI technologies.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".