Copyright Policy Options for Generative Artificial Intelligence
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
New generative artificial intelligence (AI) models, including large language models and image generators, have created new challenges for copyright policy as such models may be trained on data that includes copy-protected content.This paper examines this issue from an economics perspective and analyses how different copyright regimes for generative AI will impact the quality of content generated as well as the quality of AI training.A key factor is whether generative AI models are small (with content providers capable of negotiations with AI providers) or large (where negotiations are prohibitive).For small AI models, it is found that giving original content providers copyright protection leads to superior social welfare outcomes compared to having no copyright protection.For large AI models, this comparison is ambiguous and depends on the level of potential harm to original content providers and the importance of content for AI training quality.However, it is demonstrated that an ex-post `fair use' type mechanism can lead to higher expected social welfare than traditional copyright regimes.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it