The DOI matches its Crossref record.
Legal Challenges to Generative AI, Part I
Abstract
Questioning the legality of using in-copyright works for training data and producing outputs derived from copyrighted training data.
Overview
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In the citing paper
“Another target of scraping is open-source code, as exemplified by the 'Doe v. GitHub' case discussed in 'RQ1.2:What Are the Potential Copyright Compliance Issues-with Respect to EU Legislation-Related to the Different Methods and Data Input Phases?' (Samuelson, 2023).”
From Generative AI and intellectual property: a systematic literature review of possible issues and mitigations (2026) · cites this paper as Samuelson, 2023
In the citing paper
“In the context of the earlier mentioned lawsuit against OpenAI and GitHub, the code generated by Copilot and Codex was flagged as potentially infringing (Samuelson, 2023).”
From Generative AI and intellectual property: a systematic literature review of possible issues and mitigations (2026) · cites this paper as Samuelson, 2023
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Citations
Cited by
Bandi, A., Adapa, P. V. S. R., & Kuchi, Y. E. V. P. K. (2023) · Future Internet · doi:10.3390/fi15080260 · Cited by 638
Navigating Ethics of AI-Powered Creativity in Midjourney
Naik, T., Gostu, H., & Sharma, R. (2024) · 2024 3rd International Conference for Innovation in Technology (INOCON) · doi:10.1109/inocon60754.2024.10511571 · Cited by 8
Arnaudo, A., Coppola, R., Morisio, M., Vetrò, A., Borghi, M., Raso, R., & Khan, B. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3928 · Cited by 2
Metadata
- Authors
- Samuelson, P.
- Affiliations
- University of California, Berkeley, CA, USA
- Venue
- Communications of the ACM
- Published
- 2023-06-22
- Volume
- 66
- Issue
- 7
- Pages
- 20-23
- ISSN
- 0001-0782
- 1557-7317
- Cited by
- 22
- References
- 0
- Language
- en
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
