Knowledge Resource · Open access
Research Summary: Measuring Human Contribution in AI-Assisted Content Generation
- Original authors
- Attribution requires verification
- Original source
- arXiv — Computers and Society
- Summary & Analysis prepared by
- Aziz Shuaib Ausi
- Resource type
- Research Summary / Knowledge Resource
- Resource published on AZIZ OS
- 16 September 2026
- Reading time
- 1 min
- Publication type
- Knowledge Resource
- Availability
- Open access
About this Summary & Analysis
AZIZ OS provides independently prepared summaries and analytical interpretations of externally published research and knowledge sources. The underlying works remain attributable to their original authors and rights holders. This resource is intended to improve accessibility and understanding and does not replace the original publication.
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.
Why it matters
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.
Key insights
- The prevalence of generative AI necessitates a re-evaluation of content originality.
- Traditional notions of content generation are challenged as AI models increasingly assist humans.
- Varying degrees of human input in AI-assisted works complicate the delineation of human originality.
- A research question is posed regarding the measurement of human contribution in AI-assisted content.
- A framework based on information theory is proposed to quantify human input.
- The framework quantifies human contribution by calculating mutual information between human input and AI-assisted output relative to the self-information of the AI-assisted output.
Source
arXiv — Computers and Society — https://arxiv.org/abs/2408.14792
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- Verification ID
- ASA-EXE-2026-00599
- Version
- v1.0 · r0
- Issued
- 16 September 2026
- Resource prepared by
- Aziz Shuaib Ausi
- Resource status
- Research Summary / Knowledge Resource
- Underlying work
- Measuring Human Contribution in AI-Assisted Content Generation
- Original authors
- Attribution requires verification
- Original source
- arXiv — Computers and Society
- Provenance status
- Attribution requires verification
- Rights
- Underlying publication rights remain with the respective copyright holder(s). Refer to the original source for authoritative publication and licensing information.
This verification confirms the AZIZ OS resource record and its documented provenance. It does not establish authorship of the underlying external work.