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Cited by 156Law, AI, and Intellectual PropertyDigitalization, Law, and RegulationIntellectual Property Rights and Media

The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.

Good models borrow, great models steal: intellectual property rights and generative AI

Open access
Chesterman, Simon·2025·Policy and Society
DOI 10.1093/polsoc/puae006
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Abstract

Two critical policy questions will determine the impact of generative artificial intelligence (AI) on the knowledge economy and the creative sector. The first concerns how we think about the training of such models—in particular, whether the creators or owners of the data that are “scraped” (lawfully or unlawfully, with or without permission) should be compensated for that use. The second question revolves around the ownership of the output generated by AI, which is continually improving in quality and scale. These topics fall in the realm of intellectual property, a legal framework designed to incentivize and reward only human creativity and innovation. For some years, however, Britain has maintained a distinct category for “computer-generated” outputs; on the input issue, the EU and Singapore have recently introduced exceptions allowing for text and data mining or computational data analysis of existing works. This article explores the broader implications of these policy choices, weighing the advantages of reducing the cost of content creation and the value of expertise against the potential risk to various careers and sectors of the economy, which might be rendered unsustainable. Lessons may be found in the music industry, which also went through a period of unrestrained piracy in the early digital era, epitomized by the rise and fall of the file-sharing service Napster. Similar litigation and legislation may help navigate the present uncertainty, along with an emerging market for “legitimate” models that respect the copyright of humans and are clear about the provenance of their own creations.

Overview

Integrity signals

Registry

Crossref · 10.1093/polsoc/puae006

Cites

56 of 56 references verified0 citation errors · 0 not checkable

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Citations

Cites

Research works printed but not linked: 48

  1. Alfonsi, 2019, Taming tech giants requires fixing the revolving door, Kennedy School Review, 19, 166
  2. Alter, 2023, Franzen, grisham and other prominent authors sue OpenAI
  3. The ‘A.P.’ News, 1894, New York Times
  4. Bartlett, 1901, ‘Fake’ news for Spain
  5. Booker, 2006, The seven basic plots
  6. Brodkin, 2023, U.S. judge: Art created solely by artificial intelligence cannot be copyrighted
  7. Chomsky, 2023, The false promise of ChatGPT
  8. Compendium of U.S. Copyright Office Practices, 3rd edition, 2019
  9. Copyright Act, 1994, New Zealand
  10. Copyright Act, 2021, Singapore
  11. Copyright Amendment Act, 1994, India
  12. Copyright and Related Rights Act, 2000, Ireland
  13. Copyright, Designs and Patents Act, 1988, United Kingdom
  14. Copyright Ordinance, 1997, Hong Kong
  15. Creamer, 2023, Amazon restricts authors from self-publishing more than three books a day after AI concerns
  16. D’Auria, 2023, Rethinking intellectual property in an era of generative AI
  17. Digital Millennium Copyright Act (DMCA), 1998, (U.S.). 105-304
  18. Directive (EU) 2019/790 of the European Parliament and of the Council of 17 April 2019 on copyright and related rights in the Digital Single Market and amending Directives 96/9/EC and 2001/29/EC 2019 (EU)
  19. Drucker, 1959, The landmarks of tomorrow
  20. Edwards, 2023, Artist receives first known U.S. copyright registration for latent diffusion AI art
  21. Edwards, 2023, Why ChatGPT and Bing Chat are so good at making things up
  22. Ellis, 2010, T. S. Eliot was wrong
  23. Ellison, 2022, Who owns DALL-E images?
  24. Generative AI could raise global GDP by 7%
  25. Geng, 2023, Comparing “Deepfake” regulatory regimes in the United States, the European Union, and China, Georgetown Law Technology Review, 7, 157
  26. Graw, 2016, Painting beyond itself: The medium in the post-medium condition
  27. Guadamuz, 2022, DALL·E goes commercial, but what about copyright?
  28. Harari, 2023, AI has hacked the operating system of human civilisation
  29. Huber, 2023, Rapid advances in AI set to upend intellectual property
  30. Koestler, 1964, The act of creation
  31. Lim, 2023, A.I. & copyright – Did Singapore’s Copyright Act 2021 solve copyright problems in the training of A.I.?
  32. Menéndez, 2023, AI-generated artwork is blowing up the economics of art
  33. Metz, 2024, OpenAI says New York Times lawsuit against it is ‘without merit’
  34. Mims, 2023, AI tech enables industrial-scale intellectual-property theft, say critics
  35. Padmanabhan, A common law theory of ownership for AI-created properties
  36. Phelan, 2023, ChatGPT and intellectual property (IP) related topics
  37. Rao, 2023, AI-generated data can poison future AI models
  38. Reisner, 2023, Revealed: The authors whose pirated books are powering generative AI
  39. Reisner, 2023, These 183,000 books are fueling the biggest fight in publishing and tech
  40. Romm, 2020, Tech giants led by Amazon, Facebook, and Google spent nearly half a billion on lobbying over the past decade, new data shows
  41. Schoppert, The books used to train LLMs
  42. Seabrook, The case for and against Ed Sheeran
  43. Second request for reconsideration for refusal to register Théâtre D’opéra Spatial (SR # 1-11743923581; Correspondence ID: 1-5T5320R) 2023
  44. Silberling, Science fiction publishers are being flooded with AI-generated stories
  45. Tan, 2023, The best things in life are not for free: Copyright and generative AI learning
  46. Tan, 2023, Generative AI and copyright (part 2): Computational data analysis exception and fair use
  47. Tan, 2023, Generative AI and copyright (Part I): Copyright infringement
  48. Zikopoulos, 2012, Harness the power of big data: The IBM big data platform

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Metadata

Authors
Chesterman, S.
Affiliations
National University of Singapore (SG)
Corresponding author
Chesterman, Simon
ORCID
Chesterman, Simon
Venue
Policy and Society
Publisher
Oxford University Press (OUP)
Published
2024-02-12
Volume
44
Issue
1
Pages
23-37
ISSN
  • 1449-4035 (ISSN-L)
  • 1839-3373
DOI
10.1093/polsoc/puae006
OpenAlex
W4391754120
Cited by
156
References
49
Citations by year
2026: 60 · 2025: 66 · 2024: 27
FWCI
16.05
Type
journal article
Language
en
Open access
Licence
Locations
  • Policy and Society (publishedVersion, cc-by)
  • SSRN Electronic Journal (submittedVersion)
  • National University of Singapore (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
Ministry of Education, Singapore (MOE AcRF TIER 3 Grant (MOE-MOET32022-0001))
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
  • Social Sciences › Law › Digitalization, Law, and Regulation
  • Social Sciences › Law › Intellectual Property Rights and Media
Keywords
generative AI, intellectual property, copyright, training data, data scraping, computer-generated works, text and data mining exception, knowledge economy, creative industries, music industry, Napster, copyright litigation

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Data sources

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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.