# Exploring Perspectives on the Impact of Artificial Intelligence on the Creativity of Knowledge Work: Beyond Mechanised Plagiarism and Stochastic Parrots

arXiv: https://arxiv.org/abs/2307.10751 · HTML: https://mlsanity.com/paper/2307.10751/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 152 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://mlsanity.com/paper/2109.06977/) (2021)
- [Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals](https://mlsanity.com/paper/2209.14958/) (2022)
- [Co-Writing with Opinionated Language Models Affects Users' Views](https://mlsanity.com/paper/2302.00560/) (2023)
- [Sparks: Inspiration for Science Writing using Language Models](https://mlsanity.com/paper/2110.07640/) (2021)
- [The Creativity of Text-to-Image Generation](https://mlsanity.com/paper/2206.02904/) (2022)
- [Artificial Intelligence in the Creative Industries: A Review](https://mlsanity.com/paper/2007.12391/) (2020)
- [Beyond Text Generation: Supporting Writers with Continuous Automatic Text Summaries](https://mlsanity.com/paper/2208.09323/) (2022)
- [What is it like to program with artificial intelligence?](https://mlsanity.com/paper/2208.06213/) (2022)
- [Enough With "Human-AI Collaboration"](https://mlsanity.com/paper/2306.01615/) (2023)

## Built on by (most cited first, 11 of 11)

- [VeriPlan: Integrating Formal Verification and LLMs into End-User Planning](https://mlsanity.com/paper/2502.17898/) (2025)
- [Which Contributions Deserve Credit? Perceptions of Attribution in Human-AI Co-Creation](https://mlsanity.com/paper/2502.18357/) (2025)
- [Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop](https://mlsanity.com/paper/2508.21036/) (2025)
- [Intent Tagging: Exploring Micro-Prompting Interactions for Supporting Granular Human-GenAI Co-Creation Workflows](https://mlsanity.com/paper/2502.18737/) (2025)
- [Unlocking LLMs: Addressing Scarce Data and Bias Challenges in Mental Health](https://mlsanity.com/paper/2412.12981/) (2024)
- [Will Code Remain a Relevant User Interface for End-User Programming with Generative AI Models?](https://mlsanity.com/paper/2311.00382/) (2023)
- ["If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplace](https://mlsanity.com/paper/2602.01386/) (2026)
- [Supporting Students' Reading and Cognition with AI](https://mlsanity.com/paper/2504.13900/) (2025)
- [AI That Helps Us Help Each Other: A Proactive System for Scaffolding Mentor-Novice Collaboration in Entrepreneurship Coaching](https://mlsanity.com/paper/2508.11052/) (2025)
- [The Variance Paradox: How AI Reduces Diversity but Increases Novelty](https://mlsanity.com/paper/2508.19264/) (2025)
- [From Binary Groundedness to Support Relations: Towards a Reader-Centred Taxonomy for Comprehension of AI Output](https://mlsanity.com/paper/2604.08082/) (2026)

## Abstract (arXiv)

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. However, these issues are not new or unique to artificial intelligence. In this position paper, using examples from literary criticism, the history of art, and copyright law, I show how creativity and originality resist definition as a notatable or information-theoretic property of an object, and instead can be seen as the property of a process, an author, or a viewer. Further alternative views hold that all creative work is essentially reuse (mostly without attribution), or that randomness itself can be creative. I suggest that creativity is ultimately defined by communities of creators and receivers, and the deemed sources of creativity in a workflow often depend on which parts of the workflow can be automated. Using examples from recent studies of AI in creative knowledge work, I suggest that AI shifts knowledge work from material production to critical integration. This position paper aims to begin a conversation around a more nuanced approach to the problems of creativity and credit assignment for generative models, one which more fully recognises the importance of the creative and curatorial voice of the users of these models and moves away from simpler notational or information-theoretic views.