AI authorship?
Abstract
Considering the role of humans in copyright protection of outputs produced by artificial intelligence.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The use of LLMs raises concerns pertaining to academic integrity due to opaque training and lack of clear responsibility in authorship.
“Hence, with an increasingly opaque training procedure of LLMs and a lack of clear responsibility in terms of authorship [172], the use of large language models naturally raises a number of concerns pertaining to academic integrity.”
From The Robots Are Here: Navigating the Generative AI Revolution in Computing Education (2023) · cites this paper as [172]
Cites
Intellectual property rights in an age of electronics and information
Karel, T. A. (1987) · Government Publications Review · doi:10.1016/0277-9390(87)90091-4 · Cited by 51 · see citing works
Ginsburg, J. C. (2020) · eYLS (Yale Law School) · doi:10.15779/z38sf2mc24 · Cited by 19 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The Robots Are Here: Navigating the Generative AI Revolution in Computing Education
Prather, J., Denny, P., Leinonen, J., Becker, B. A., Albluwi, I., Craig, M., Keuning, H., Kiesler, N., Kohn, T., Luxton-Reilly, A., et al. (2023) · Proceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education · doi:10.1145/3623762.3633499 · Cited by 396 · see citing works
Draxler, F., Werner, A., Lehmann, F., Hoppe, M., Schmidt, A., Buschek, D., & Welsch, R. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3637875 · Cited by 186 · see citing works
“Rights allocation may indeed be entirely subjective, as evidenced by strikingly different legislation across countries [90].”
BackgroundGAFFAR, H., & ALBARASHDI, S. (2025) · Asian Journal of International Law · doi:10.1017/s2044251323000735 · Cited by 91 · see citing works
Kazimi, J., & Thalwal, H. (2024) · 2024 7th International Conference on Green Technology and Sustainable Development (GTSD) · doi:10.1109/gtsd62346.2024.10675218 · Cited by 5 · see citing works
- Authors
- Samuelson, P.
- Affiliations
- University of California, Berkeley (US)
- Corresponding author
- Samuelson, Pamela
- ORCID
- Samuelson, Pamela
- Venue
- Communications of the ACM
- Published
- 2020-06-18
- Volume
- 63
- Issue
- 7
- Pages
- 20-22
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W3173585017
- Cited by
- 25
- References
- 2
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 4 · 2023: 4 · 2022: 2 · 2021: 2
- FWCI
- 0.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Communications of the ACM (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Political Science and International Relations › Intellectual Property Rights and Media
- Keywords
- AI authorship, copyright, artificial intelligence, authorship, intellectual property
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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