Mining, Scraping, Training, Generating: Copyright Implications of Generative <scp>AI</scp>
Bibliographic record
Abstract
ABSTRACT Generative AI (GenAI) impacts the ways we create, engage with, and understand creative and intellectual works. These new forms of sociotechnical (inter)action pose challenges for existing legal regimes, ethical frameworks, and social relationships. This research undertakes an in‐depth copyright analysis of GenAI based on U.S. law, focusing on its fair use doctrine and conceptions of transformation. This work finds that courts' characterization of uses as primarily either “expressive” or “mediating” is an important, though often implicit, factor in their decisions. Furthermore, while “transformative use” has dominated fair use decisions for the past thirty years, findings from this research suggest that GenAI may usher in a renewed emphasis on the doctrine's market harms element which, in application, may be dispositive with respect to GenAI outputs. This work concludes by offering recommendations aimed at clarifying that the value of copyright arises from social and relational aspects of creative practice and sociotechnical transformation. Arguments and rationales that (over)emphasize atomization and algorithmic decontextualization of the material properties of creative works are unlikely to attend to the underlying purpose of the Act: “[t]o promote the Progress of Science and the useful Arts”.
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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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".