Measuring Human Contribution in AI-Assisted Content Generation

Research from arXiv highlights the increasing challenge of defining originality and measuring human contribution in content generated with the assistance of Artificial Intelligence (AI). The study introduces a novel framework, grounded in information theory, to quantify the proportional informational contribution of humans in AI-assisted content generation by calculating mutual information relative to the self-information of the output.

The ability to accurately measure human contribution in AI-assisted content generation is critical for intellectual property rights, attribution, and the ethical development of AI. This research provides a foundational approach to addressing these complex issues, which will become increasingly important as AI adoption grows across various sectors.

The prevalence of generative AI necessitates a re-evaluation of content originality.

Forward consideration, not a verified fact.