preprints_ui: q73vy_v1
Data license: ODbL (database) & original licenses (content) · Data source: Open Science Framework
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| q73vy_v1 | “Heart on My Sleeve”: From Memorization to Duty | Can a machine learning model infringe on a copyright—do machine learning models store protected content? This work-in-progress law review Article focuses on empirical data developed, in part, to answer that question: yes. A set of unconditional image generators, diffusion models (n = 14), are trained on small slices of a dataset consisting of celebrities’ faces. The synthetic data output from these generators is then compared to training data using a variety of similarity metrics. As the empirical data shows, the question is not can models contain copyrighted works, but do models contain copyright works. In some cases, there is a 99% chance that a model will generate an image nearly identical to its training data; in other cases, even after 10,000 generations, a model does not produce any images that may be considered identical (though finding similarity is nonetheless possible). This Article uses the empirical data to argue for a series of duties to be placed on model owners. | 2024-09-24T02:08:43.582036 | 2024-09-24T14:10:05.146126 | 2024-09-24T02:12:33.119737 | 2024-07-25T04:00:00 | lawarxiv | 1 | accepted | 1 | 1 | https://doi.org/10.31228/osf.io/q73vy | CC0 1.0 Universal | Copyright | ["Copyright"] | Nathan Reitinger | [{"id": "t9vu7", "name": "Nathan Reitinger", "index": 0, "orcid": "", "bibliographic": true}] | Nathan Reitinger | Law | [{"id": "59bacc7e54be810341615c32", "text": "Law"}] | https://osf.io/download/66f21f5add5f30b3669e8544 | 0 | available | not_applicable | [] | 2025-04-09T21:06:18.956598 |