Mining, Scraping, Training, Generating: Copyright Implications of Generative <scp>AI</scp>
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
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”.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| 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