Exploring Perspectives on the Impact of Artificial Intelligence on the Creativity of Knowledge Work: Beyond Mechanised Plagiarism and Stochastic Parrots
Open accessAbstract
Artificial Intelligence (AI), and in particular generative models, are transformative tools for knowledge work. They problematise notions of creativity, originality, plagiarism, the attribution of credit, and copyright ownership. Critics of generative models emphasise the reliance on large amounts of training data, and view the output of these models as no more than randomised plagiarism, remix, or collage of the source data. On these grounds many have argued for stronger regulations on the deployment, use, and attribution of the output of these models.
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Prior work has identified the work of understanding and evaluating GenAI output as a key aspect of working with GenAI.
“Prior work has identified the work of understanding and evaluating GenAI output as a key aspect of working with GenAI [29,46,114,130].”
From The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers (2025) · cites this paper as [114]
Prior work has suggested GenAI requires knowledge workers to perform 'critical integration'.
“Prior work has suggested GenAI requires knowledge workers to perform”
From The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers (2025) · cites this paper as [114]
AI shifts knowledge work from material production to critical integration of ideas.
“This is supported by earlier work by Sarkar (2023) suggesting that AI shifts knowledge work from material production to critical integration of ideas.”
From If ChatGPT can do it, where is my creativity? generative AI boosts performance but diminishes experience in creative writing (2025) · cites this paper as Sarkar, 2023
Prior work has identified factors that affect people's sense of ownership over work produced with AI assistance, including types of contributions, amount of material contributed, process factors such as critical integration and control, and the role of AI in the writing process.
“Some of these factors regard characteristics of the contributions themselves, such as the types of contributions made [33, 71, 84, 92] and the amount of material contributed [44].”
From Which Contributions Deserve Credit? Perceptions of Attribution in Human-AI Co-Creation (2025) · cites this paper as [72]
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Self-citation
- Authors
- Sarkar, A.
- Affiliations
- Microsoft (United Kingdom) (GB)
- Corresponding author
- Sarkar, Advait
- ORCID
- Sarkar, Advait
- Venue
- Proceedings of the 2nd Annual Meeting of the Symposium on Human-Computer Interaction for Work
- Publisher
- ACM
- Published
- 2023-06-13
- Pages
- 1-17
- OpenAlex
- W4385003312
- Cited by
- 50
- References
- 68
- Citations by year
- 2026: 14 · 2025: 25 · 2024: 8 · 2023: 3
- FWCI
- 8.86
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Annual Symposium on Human-Computer Interaction for Work (CHIWORK) (publishedVersion, public-domain)
- arXiv (Cornell University) (submittedVersion, public-domain)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Political Science and International Relations › Literary and Philosophical Studies
- Keywords
- artificial intelligence, generative models, creativity, knowledge work, plagiarism, originality, attribution, copyright ownership, training data, AI regulation, large language models
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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