Intelligent Agents: Authors, Makers, and Owners of Computer-Generated Works in Canadian Copyright Law
Bibliographic record
Abstract
The central objective of this article is to propose a clarification of copyright law as applied to works created by intelligent agents. In Part I, the concepts of artificial intelligence and intelligent agents are introduced. Part II identifies the challenges that are presented to the tests of originality and authorship in the application of copyright to works generated by intelligent agents. It is argued that works created by intelligent agents may meet the tests of originality and authorship. It is also argued that the con- cepts of ‘‘author’’, ‘‘owner’’, and ‘‘maker’’ are distinct from one another in Canadian copyright law. Part III addresses copyright policy arguments. It is shown that intelligent agents may be authors of works but not owners of copy- right, and that there is no clear candidate who should be designated the maker of works created by intelligent agents. The role of the public domain is also considered, and it is concluded that the best solution is for no copy- right ownership to be vested in anyone. Database protec- tion legislation is examined in Part IV. The paper con- cludes with some suggestions that should be considered as part of the ongoing process of Canadian copyright law reform.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.026 | 0.039 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".