Copyright Issues in the Artworks Generated by Artificial Intelligence
Bibliographic record
Abstract
This article explores the upcoming issues and legal challenges in copyright law brought by AI-generated artworks. As AI technologies improve, the creation of art by AI has raised problems regarding authorship, originality, and the application of existing copyright frameworks. By analyzing cases happened in different region about copyright in AI-generated art, it is discovered that different attitudes toward AI-generated artworks under current copyright framework. While the United States show a relatively conservative stance, insisting that the role of author must be human, other countries such as Canada and China began to admit the authorship of AI, accepting AI as a way to achieve creativity and originality. Based on the existing situation, the article provided possible solutions, aiming to protect copyright of creative artworks generated by AI and accept AI as artistic tool that could increase efficiency and creativity.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".