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Redefining boundaries in innovation and knowledge domains: Investigating the impact of generative artificial intelligence on copyright and intellectual property rights
Open accessAbstract
The rapid integration of generative AI (GenAI) into industries and society has prompted a re-evaluation of copyright and intellectual property rights (IPR) frameworks. GenAI's ability to produce original content using data from human-created sources raises critical ethical and legal concerns. Current copyright and IPR frameworks, designed around human authorship, are insufficient to address these challenges. This study, using a multi-perspective approach, explores GenAI's disruptive potential in replicating or transforming copyrighted materials, challenging established IPR norms. Findings highlight gaps in legislation and the opacity of GenAI platforms. To address these issues, this study presents a Dynamic Ethical Framework linked to a future global fair use policy, aiming to guide responsible GenAI development and use. By incorporating insights from domain experts, this study contextualizes emerging challenges and potential solutions within broader societal and technological trends. That said, this study calls for international collaboration and further research to reform IPR related laws and frameworks, ensuring they remain relevant and equitable in a GenAI-driven era.
Checked 2026-11-12 · 1 reference not checked yet
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TL;DR
This multi-expert paper argues that current copyright and intellectual property rights frameworks, built around human authorship, are insufficient for generative AI, and it proposes a Dynamic Ethical Framework linked to a future global fair use policy. It synthesizes legal, policy, and practice perspectives to call for international collaboration and further research on GenAI-related copyright reform.
Key findings
- The paper finds gaps in legislation and opacity in GenAI platforms, with current copyright/IPR frameworks unable to address GenAI’s use of human-created data and its generation of content that challenges authorship, ownership, and fair use.
- It reports that GenAI’s economic impact is estimated at US$2.6–$4.4 trillion in annual value added, increasing AI’s total economic impact by 40%; ChatGPT reached 100 million users in two months; and recorded music alone is a global annual market worth $26bn.
- Legal disputes illustrate the uncertainty: Getty Images accused Stability AI of using more than 12 million copyrighted images, and cases such as New York Times v. OpenAI test fair use, transformation, and market impact.
- The authors propose a Dynamic Ethical Framework and “Global Fair AI Use” policy covering GenAI platform developers, authorship, and ownership, and call for international collaboration to reform IPR laws.
Methods
- Design: a multiperspective expert-based approach, following prior work by von Foerster and Dwivedi et al., inviting specialists from academia and practice to contribute perspectives on GenAI, copyright, and IPR.
- Data/sample: eleven invited expert contributions, plus legal cases, policy documents, and literature; no primary empirical dataset is collected.
- Analysis: thematic analysis of the contributions, organized around emerging challenges, legal complexities, creativity/authorship/ownership, regulation and policy, and future adaptation; the discussion develops the Dynamic Ethical Framework and a proposed “Global Fair AI Use” policy.
Limitations
- Stated in the paper: the multi-expert format may produce overlapping narratives across perspectives; the paper’s length is a limitation; contributions were limited to eleven due to the specialised nature of the topic.
- Evident from the text: the study is conceptual/editorial rather than empirical, and the proposed Dynamic Ethical Framework is not tested or validated; legal coverage is jurisdiction-specific and not a systematic global legal review.
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In the paper
“The absence of a traditional mechanism of authorship in AI-generated content creation, could potentially shift the economic benefits away from human creators towards those who own or operate the AI technologies and platforms (Israhadi, 2023).”
§ Redefining boundaries in innovation and knowledge domains: Investigating the impact of generative artificial intelligence on copyright and intellectual property rights > Introduction
What the sources say
- Check pendingcites Israhadi, 2023 source
hedgedstates a possible cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“GenAI's ability to produce original content using data from human-created sources raises critical ethical and legal concerns.”
- Also stated in: § Introduction · plain
“This has profound legal and ethical consequences related to the protection of human creativity, authorship, and content ownership (Frosio, 2024).”
§ Redefining boundaries in innovation and knowledge domains: Investigating the impact of generative artificial intelligence on copyright and intellectual property rights > Abstract
What the sources say
- Check pendingcites Frosio, 2024 source
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Introduction · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Current copyright and IPR frameworks, designed around human authorship, are insufficient to address these challenges.”
§ Redefining boundaries in innovation and knowledge domains: Investigating the impact of generative artificial intelligence on copyright and intellectual property rights > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption not shown here.
In the paper
“The emergence of AI as a non-human creator, directly challenges the existing legal infrastructure built around the human creator (Frosio, 2024;Liu et al., 2023).”
§ Redefining boundaries in innovation and knowledge domains: Investigating the impact of generative artificial intelligence on copyright and intellectual property rights > Introduction
What the sources say
- Check pendingcites Frosio, 2024 source
- Not matchedcites Liu et al., 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Within the current legal frameworks, GenAI applications may be liable for copyright infringement if they both had access to the original works and GenAI generated output is substantially similar to the original works (Zirpoli, 2023).”
§ Redefining boundaries in innovation and knowledge domains: Investigating the impact of generative artificial intelligence on copyright and intellectual property rights > Legal Complexities
What the sources say
- Not matchedcites Zirpoli, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedstates a possible cause- Asserted: passes — The authors put it forward as true.
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Metadata
- Authors
- Al-Busaidi, A. S., Raman, R., Hughes, L., Albashrawi, M. A., Malik, T., Dwivedi, Y. K., Al- Alawi, T., AlRizeiqi, M., Davies, G., Fenwick, M., Gupta, P., Gurpur, S., Hooda, A., Jurcys, P., Lim, D., Lucchi, N., Misra, T., Raman, R., Shirish, A., & Walton, P.
- Affiliations
- Sultan Qaboos University (OM)
- Edith Cowan University (AU)
- Swansea University (GB)
- King Fahd University of Petroleum and Minerals (SA)
- University of Wales Trinity Saint David (GB)
- Kyushu University (JP)
- Management Development Institute (IN)
- Symbiosis International University (IN)
- O. P. Jindal Global University (IN)
- Pennsylvania State University (US)
- Universitat Pompeu Fabra (ES)
- Université Paris-Saclay (FR)
- Capgemini (United Kingdom) (GB)
- ORCID
- Venue
- Journal of Innovation & Knowledge
- Publisher
- Elsevier BV
- Published
- 2024-10
- Volume
- 9
- Issue
- 4
- Pages
- 100630
- ISSN
- 2444-569X (ISSN-L)
- OpenAlex
- W4404867434
- Cited by
- 95
- References
- 91
- Citations by year
- 2026: 48 · 2025: 43 · 2024: 4
- FWCI
- 9.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Innovation & Knowledge (publishedVersion, cc-by-nc-nd)
- SSRN Electronic Journal (submittedVersion)
- Econstor (Econstor) (submittedVersion, cc-by-nc-nd)
- Australasian Journal of Paramedicine (submittedVersion, cc-by-nc-nd)
- HAL (submittedVersion)
- Cronfa (Swansea University) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Institutional Research Information System University of Ferrara (University of Ferrara) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Social Sciences › Law › Intellectual Property Law
- Social Sciences › Law › Intellectual Property Rights and Media
- Keywords
- generative AI, copyright, intellectual property, AI-generated content, authorship, fair use, AI ethics, AI regulation, international intellectual property law
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.
A language model drew the claims from the paper on 2026-11-12, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-12 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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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