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Core Question
When AI can generate answers and systems can execute actions, who has the authority to judge?
Why This Exists
AI changes the speed and scale of output. It does not remove responsibility, consequence, or the need to identify who has authority before action occurs.
The risk is confusing generated advice with accountable decisions. Decision Legitimacy studies the boundary between output, recommendation, action, and decision.
Explore the Site
Cognitive Assets
Persistent human-AI collaboration can create context, memory structures, and judgment patterns. The value is continuity of judgment across time.
Decision Legitimacy Framework
A decision is legitimate only when authority, responsibility, and automation boundaries are clear. The framework separates output, recommendation, action, and decision.
Judgment Applications
Applied judgment clarifies what should be decided before costly execution begins. It is used for business, technical, and AI project decisions.
Reports & Whitepapers
Reports turn judgment models into reviewable documents, risk maps, and decision structures. They provide public artifacts for complex questions.
Cognitive Assets
The Emergence of Persistent Human-AI Cognition
The Cognitive Assets whitepaper examines how repeated human-AI collaboration can create durable structures of context, memory, judgment, and continuity. These structures are valuable only when judgment authority remains explicit.
Public release: GitHub · Zenodo DOI
Public Assets
- Cognitive Assets Whitepaper v1.0
- GitHub Release
- Zenodo DOI: 10.5281/zenodo.20232547
- Shopify 3PL Routing Risk Whitepaper
- Books
Applied Judgment
Practical applications include:
- Business Go / No-Go Judgment
- Loss-Making Store Exit Judgment
- AI Project Failure Diagnosis
- Shopify 3PL Routing Diagnosis
- Whitepaper as Trust Asset
Related report: Shopify 3PL Routing Risk Whitepaper
Need an independent judgment review?
This site also supports applied judgment work where the problem requires structured judgment rather than routine execution.