Measuring Human Contribution in AI-Assisted Content Generation
This work addresses the challenge of delineating originality in AI-assisted content for creators and evaluators, but it is incremental as it builds on existing information theory concepts.
The study tackled the problem of measuring human contribution in AI-assisted content generation by introducing an information theory-based framework, and the results showed that the proposed measure effectively discriminates varying degrees of human contribution across multiple creative domains.
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.