Measuring Human Contribution in AI-Assisted Content Generation
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910825335226368 |
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| author | Xie, Yueqi Qi, Tao Yi, Jingwei Yang, Xiyuan Whalen, Ryan Huang, Junming Ding, Qian Xie, Yu Xie, Xing Wu, Fangzhao |
| author_facet | Xie, Yueqi Qi, Tao Yi, Jingwei Yang, Xiyuan Whalen, Ryan Huang, Junming Ding, Qian Xie, Yu Xie, Xing Wu, Fangzhao |
| contents | With the growing prevalence of generative artificial intelligence (AI), an increasing amount of content is no longer exclusively generated by humans but by generative AI models with human guidance. This shift presents notable challenges for the delineation of originality due to the varying degrees of human contribution in AI-assisted works. This study raises the research question of measuring human contribution in AI-assisted content generation and introduces a framework to address this question that is grounded in information theory. By calculating mutual information between human input and AI-assisted output relative to self-information of AI-assisted output, we quantify the proportional information contribution of humans in content generation. Our experimental results demonstrate that the proposed measure effectively discriminates between varying degrees of human contribution across multiple creative domains. We hope that this work lays a foundation for measuring human contributions in AI-assisted content generation in the era of generative AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_14792 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Measuring Human Contribution in AI-Assisted Content Generation Xie, Yueqi Qi, Tao Yi, Jingwei Yang, Xiyuan Whalen, Ryan Huang, Junming Ding, Qian Xie, Yu Xie, Xing Wu, Fangzhao Computers and Society Artificial Intelligence Computation and Language With the growing prevalence of generative artificial intelligence (AI), an increasing amount of content is no longer exclusively generated by humans but by generative AI models with human guidance. This shift presents notable challenges for the delineation of originality due to the varying degrees of human contribution in AI-assisted works. This study raises the research question of measuring human contribution in AI-assisted content generation and introduces a framework to address this question that is grounded in information theory. By calculating mutual information between human input and AI-assisted output relative to self-information of AI-assisted output, we quantify the proportional information contribution of humans in content generation. Our experimental results demonstrate that the proposed measure effectively discriminates between varying degrees of human contribution across multiple creative domains. We hope that this work lays a foundation for measuring human contributions in AI-assisted content generation in the era of generative AI. |
| title | Measuring Human Contribution in AI-Assisted Content Generation |
| topic | Computers and Society Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2408.14792 |