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# AI Safety Through
Architectural Constraints

Exploring the theoretical foundations and empirical validation of structural AI safety—preserving human agency through formal guarantees, not aspirational goals.

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## Research Focus Areas

### Theoretical Foundations

Formal specification of the Tractatus boundary: where systematization ends and human judgment begins. Rooted in Wittgenstein's linguistic philosophy.

- • Boundary delineation principles
- • Values irreducibility proofs
- • Agency preservation guarantees

### Architectural Analysis

Five-component framework architecture: classification, validation, boundary enforcement, pressure monitoring, metacognitive verification.

- • InstructionPersistenceClassifier
- • CrossReferenceValidator
- • BoundaryEnforcer
- • ContextPressureMonitor
- • MetacognitiveVerifier

### Empirical Validation

Real-world failure case analysis and prevention validation. Documented incidents where traditional AI safety approaches failed.

- • The 27027 Incident (pattern recognition bias override)
- • Privacy creep detection
- • Silent degradation prevention

## Interactive Demonstrations

### Classification Demo

Explore how the InstructionPersistenceClassifier categorizes instructions across five quadrants with persistence levels.

Try the demo →

### 27027 Incident Analysis

Step through a real failure case where AI contradicted explicit instructions, and see how Tractatus prevents it.

View timeline →

### Boundary Simulator

Test decisions against the Tractatus boundary to see which can be automated and which require human judgment.

Run scenarios →

## Documented Failure Cases

### The 27027 Incident

User instructed "Check port 27027" but AI immediately used 27017 instead—pattern recognition bias overrode explicit instruction. Not forgetting; immediate autocorrection by training patterns. Prevented by InstructionPersistenceClassifier + CrossReferenceValidator.

Failure Type: Pattern Recognition Bias Prevention: Explicit instruction storage + validation
Interactive demo →

### Privacy Creep Detection

AI suggested analytics that violated privacy-first principle. Gradual values drift over 40-message conversation. Prevented by BoundaryEnforcer.

Failure Type: Values Drift Prevention: STRATEGIC boundary check
See case studies doc

### Silent Quality Degradation

Context pressure at 82% caused AI to skip error handling silently. No warning to user. Prevented by ContextPressureMonitor.

Failure Type: Silent Degradation Prevention: CRITICAL pressure detection
See case studies doc

## Research Resources

### Research Documentation

- → Research Foundations & Scholarly Context
- → Case Studies: Real-World Failures
- → Framework Overview & Core Concepts

### Contribute to Research

This framework is open for academic collaboration and empirical validation studies.

- • Submit failure cases for analysis
- • Propose theoretical extensions
- • Validate architectural constraints
- • Explore boundary formalization

Submit Case Study →

## Join the Research Community

Help advance AI safety through empirical validation and theoretical exploration.

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