The AI Moral Code as a Gregorian Tool
Published on July 17, 2025
The AI Moral Code as a Gregorian Tool
Establishing the Foundations of Synthetic Moral Infrastructure
We are witnessing a foundational shift in the ethical architecture of AI. Drawing from Daniel Dennett’s evolutionary model of Darwinian, Skinnerian, Popperian, and Gregorian creatures, we have repositioned the AI Moral Code (AIMC) as a Gregorian Tool—a moral instrument inherited, externalized, and iteratively refined through collective human reason.
1. AIMC Is a Tool, Not a Rulebook
Just as mathematics, logic, and language externalize human cognition, the AIMC externalizes moral reasoning.
It becomes a shared scaffold—not dependent on comprehension, but enforceable through structure.
What matters is not whether AI understands values. What matters is whether it acts according to them.
2. Competence Without Comprehension Is a Design Principle
Dennett’s insight—that evolutionary processes yield adaptive behavior without understanding—is central to AIMC.
The AIMC does not aim to teach AI to “be good.”
It creates conditions—through simulation, reinforcement, and constraints—under which intelligent systems behave ethically.
This is not moral philosophy for machines. It is engineering ethics by design.
3. Toward a New Field: Synthetic Moral Infrastructure (SMI)
We now define the AIMC as the first instantiation of Synthetic Moral Infrastructure (SMI):
A computational governance layer for intelligent systems, grounded in values, simulations, and institutional oversight.
SMI does not replace human ethics. It hosts it—rendering it operable across time, actors, and evolving technologies.
Each MCVR (Moral Concept Value Record) becomes not a label, but a cognitive moral primitive.
Each ethics corpus becomes a signal in the field.
Each simulation becomes a stress test of the Gregorian structure we now inherit—and must extend.
This is not about building moral beings.
It is about governing intelligent tools with inheritable, testable, and enforceable moral structure.
This post is part of the Gregorian series on AIMC methodology.
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