A Protocol Framework for Accountable Human–AI Collaboration
Version 2.0 · October 2025
Checkpoint-Based Governance (CBG) defines how humans and AI systems collaborate responsibly through structured oversight, documented arbitration, and measurable accountability.
It bridges the gap between AI regulation that demands oversight and organizations that need practical governance systems.
CBG introduces a structured pattern of decision checkpoints that ensure humans verify, decide, and log outcomes before AI-driven actions proceed — creating visible, measurable accountability without slowing innovation.
Regulations like the EU AI Act and frameworks such as NIST AI RMF call for “effective human oversight,” yet offer little guidance on implementation.
CBG provides the how: an operational model that scales across teams, industries, and risk levels.
- Maintains human authority over AI outputs
- Prevents automation bias and drift
- Produces audit-ready documentation
- Delivers 15–20% performance gains in structured collaboration environments
- Scales for low- to high-risk use cases
CBG operates through four repeating phases at each checkpoint:
- AI Contribution – AI provides an output or recommendation
- Checkpoint Evaluation – Output is reviewed against set criteria
- Human Arbitration – Human approves, modifies, or rejects
- Decision Logging – Action proceeds only after the decision and rationale are recorded
This enforces traceable accountability across all AI–human interactions.
| Framework | Purpose | Domain |
|---|---|---|
| HAIA-RECCLIN | Role-based checkpoint collaboration | Multi-AI workflows |
| HAIA-SMART | Content quality & brand governance | Marketing & communications |
| Factics | Fact-to-KPI verification | Measurement & outcomes |
| HEQ / FID | Human-AI collaboration metrics | Research & assessment |
Together, these form the HAIA Systems Suite, demonstrating CBG’s scalability from governance structure to content quality and measurement.
📄 Included:
Checkpoint-Based Governance: An Implementation Framework for Accountable Human-AI Collaboration (v2.0)
Defines the theoretical foundation, methodology, and reference mapping to ISO/IEC 42001, NIST AI RMF, and ITU 2025 governance standards.
Licensed under the Apache License, Version 2.0.
You may use, modify, and distribute this work provided that proper attribution is included and derivative works clearly identify changes.
© 2025 Basil C. Puglisi
Human-AI Collaboration Strategist | Creator of HAIA-RECCLIN, HAIA-SMART, and Factics
https://basilpuglisi.com
If referencing this repository or position paper:
Puglisi, B. C. (2025). Checkpoint-Based Governance: An Implementation Framework for Accountable Human–AI Collaboration (v2.0). GitHub Repository: https://github.com/basilpuglisi/Checkpoint-Based-Governance
Pull requests are welcome for:
- Implementation templates
- Visualization tools for decision checkpoints
- Drift detection or audit trail analysis utilities
All contributions must maintain human arbitration checkpoints before merging.
“AI doesn’t replace accountability — it tests it.
Checkpoint-Based Governance ensures humans stay visibly and verifiably in control.”
— Basil C. Puglisi