Governance-First Frameworks for AI-Human Task Integrity
AHI FIN stands for Artificial × Human Intelligence Framework for Integrity & Neutrality.
It is a governance-first initiative designed to operationalise trust and accountability in AI-human workflows.
Current AI governance frameworks:
- Focus on principles and risk categories, not task-level allocation logic.
- Lack operational clarity for human vs AI boundaries.
- Fail to provide auditability and proportional oversight.
AHI FIN solves these gaps by:
- Embedding governance logic into task design.
- Enabling atomic decomposition for transparency.
- Applying risk-based oversight proportional to reasoning complexity and data ambiguity.
This release is a working draft and will continue to evolve.
While the structure and logic have been carefully designed, some sections may require refinement.
Treat this version as preliminary and not yet production-certified.
This work is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).
You are free to share and adapt this material, provided you give appropriate credit.
© Kelvin Chau, 2025
This work is part of Project Ahi Fin [(https://github.com/kfkchau/project-ahi-fin)].
Licensed under https://creativecommons.org/licenses/by/4.0/.
For attribution, citation, or inquiries, please refer to:
🔗 https://au.linkedin.com/in/kfkchau