Semantic Governance vs
Constitutional AI
Constitutional AI embeds principles at training time through self-critique. Semantic governance asks: what if different stakeholders could specify intent dynamically at runtime?
What Constitutional AI Got Right
Before we compare, let's acknowledge the breakthrough.
Constitutional AI, introduced by Anthropic in 2022, was a significant step forward from pure RLHF. Instead of relying solely on human preference data, it gave the model explicit principles (a "constitution") and trained it to critique its own outputs against those principles.
This made values more explicit than pure preference learning. The self-critique mechanism proved that AI systems could reason about their own behavior relative to stated values—a key insight for interpretable alignment.
The key insight: Instead of hoping values emerge from preferences, state them explicitly. Then use the model's own capabilities to enforce consistency with those stated values.
The Structural Gap Semantic Governance Addresses
Constitutional AI fixes values at training time. Semantic governance makes intent dynamic.
Constitutional AI
Define a set of principles (the constitution), train the model to critique outputs against these principles, fine-tune to follow them.
Governance Flow
Semantic Governance
Different stakeholders specify intent as structured artifacts. Intent flows through delegation chains. Actions are traced back to sources.
Governance Flow
An Evolution, Not a Replacement
Each approach solved a real problem. Semantic governance addresses gaps the others couldn't.
Hardcoded Rules
Write explicit if-then rules → AI follows exactly
Limitation: Can't cover every case, breaks on ambiguity
Constitutional AI
Write principles → Model self-critiques against them
Limitation: Principles fixed at training, developer-defined only
Semantic Governance
Stakeholders specify intent → Compose at runtime
Limitation: Requires explicit intent architecture
Three Key Differences
Who Specifies Values?
Constitutional AI: Developers write the constitution before training. Users receive a pre-aligned system with values they can't modify.
Semantic Governance: Multiple stakeholders can specify intent— organizations, regulators, users. The system manages how these intents compose and handles conflicts explicitly.
When Are Values Fixed?
Constitutional AI: Values are frozen at training time. Changing them requires retraining or fine-tuning—expensive and slow.
Semantic Governance: Intent artifacts can be updated dynamically. The system adapts to new intent specifications without retraining the underlying model.
How Are Conflicts Resolved?
Constitutional AI: The model self-critiques against principles, but conflict resolution is implicit in the training process. Trade-offs are baked into weights, not inspectable.
Semantic Governance: Conflicts between intents are surfaced explicitly. Priority schemas determine how competing values are balanced, and the resolution is traceable—you can see exactly which intent took precedence and why.
The Multi-Context Problem
AI systems operate in different contexts with different stakeholders. A single constitution can't anticipate all deployment scenarios.
The Problem With One Constitution
Constitutional AI works well when there's a single, clear set of values that apply universally. But real-world deployment involves:
Different Contexts
A medical AI needs different constraints than an entertainment AI. One constitution can't serve all use cases.
Multiple Stakeholders
Users, organizations, and regulators all have legitimate but different intents that need to compose.
Evolving Requirements
Regulations change, organizational policies update, user needs shift. Retraining for each change doesn't scale.
Semantic Governance's Approach
Instead of one constitution for all contexts, semantic governance creates a layer where context-specific intent can be specified and composed:
Layered Intent
Base model capabilities + organizational intent + regulatory constraints + user preferences. Each layer is explicit and auditable.
Runtime Composition
Intent artifacts compose at runtime. Change a regulation? Update one artifact. New organizational policy? Add it to the stack. No retraining required.
Feature Comparison
| Feature | Constitutional AI | Semantic Gov |
|---|---|---|
| Value Source | Developer-written principles | Stakeholder-specified intent |
| Principle Format | Natural language rules | Structured semantic artifacts |
| When Values Are Set | Training time (frozen) | Runtime (dynamic) |
| Conflict Resolution | Model self-critique | Explicit priority schemas |
| Provenance | Embedded in weights | Traceable artifacts |
| Multi-Stakeholder | Single author (developer) | Multiple intent sources |
| Adaptability | Requires retraining | Dynamic intent updates |
| Deployment Maturity | Production-proven (Claude) | Research phase |
The Real Choice
These aren't competing approaches—they can layer together.
Use Constitutional AI When:
- •Building a general-purpose assistant
- •Values are stable and universal
- •Single organization controls deployment
- •Retraining is feasible when values change
Use Semantic Governance When:
- •Multiple stakeholders have different intents
- •Context-specific constraints are needed
- •Requirements change frequently
- •Audit trails are required
The Layered Architecture
A powerful approach uses Constitutional AI as the base layer—establishing fundamental safety and helpfulness properties. Then semantic governance adds context-specific intent on top—organizational policies, regulatory constraints, user preferences. The constitution provides the foundation; semantic governance provides the customization.
The Theoretical Foundation
IRSA's work on semantic governance builds on Constitutional AI's insight that values should be explicit—but extends it to ask who gets to specify those values and when. The answer matters for accountability, adaptability, and democratic governance of AI systems.
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