Intelligent Agents: Authors, Makers, and Owners of Computer-Generated Works in Canadian Copyright Law
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it