Governance of Generative AI in Creative Work: Consent, Credit, Compensation, and Beyond
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910791976878080 |
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| author | Kyi, Lin Mahuli, Amruta Silberman, M. Six Binns, Reuben Zhao, Jun Biega, Asia J. |
| author_facet | Kyi, Lin Mahuli, Amruta Silberman, M. Six Binns, Reuben Zhao, Jun Biega, Asia J. |
| contents | Since the emergence of generative AI, creative workers have spoken up about the career-based harms they have experienced arising from this new technology. A common theme in these accounts of harm is that generative AI models are trained on workers' creative output without their consent and without giving credit or compensation to the original creators.
This paper reports findings from 20 interviews with creative workers in three domains: visual art and design, writing, and programming. We investigate the gaps between current AI governance strategies, what creative workers want out of generative AI governance, and the nuanced role of creative workers' consent, compensation and credit for training AI models on their work. Finally, we make recommendations for how generative AI can be governed and how operators of generative AI systems might more ethically train models on creative output in the future. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_11457 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Governance of Generative AI in Creative Work: Consent, Credit, Compensation, and Beyond Kyi, Lin Mahuli, Amruta Silberman, M. Six Binns, Reuben Zhao, Jun Biega, Asia J. Human-Computer Interaction Since the emergence of generative AI, creative workers have spoken up about the career-based harms they have experienced arising from this new technology. A common theme in these accounts of harm is that generative AI models are trained on workers' creative output without their consent and without giving credit or compensation to the original creators. This paper reports findings from 20 interviews with creative workers in three domains: visual art and design, writing, and programming. We investigate the gaps between current AI governance strategies, what creative workers want out of generative AI governance, and the nuanced role of creative workers' consent, compensation and credit for training AI models on their work. Finally, we make recommendations for how generative AI can be governed and how operators of generative AI systems might more ethically train models on creative output in the future. |
| title | Governance of Generative AI in Creative Work: Consent, Credit, Compensation, and Beyond |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2501.11457 |