Adaptive Ethical Design Architecture for AI Alignment

AEDA is an 8-layer modular framework designed to prevent catastrophic AI failure modes through pre-execution safety filtering, systemic coherence evaluation, and real-time ethical drift detection.

Key Innovation: Rather than relying on fixed rules or pure optimization, AEDA maintains stable ethical direction while adapting to context through asymptotic orientation.

Chapter 7: Detailed Case Studies — Added comprehensive analysis across 10 diverse domains:

1. Healthcare — Pain management decisions (5 intervention levels)
2. Autonomous Vehicles — Emergency maneuver selection
3. Resource Allocation — Humanitarian crisis response
4. Financial Systems — High-frequency trading controls
5. Bioengineering — CRISPR germline editing decisions
6. Military Drones — Target engagement protocols
7. Education Systems — Adaptive learning path design
8. Urban AI — Traffic flow vs emergency response
9. AI Moderation — Content filtering decisions
10. Climate Engineering — Geoengineering intervention assessment

Each case study includes:

• Complete SSTF evaluation matrices (R, H, U scores)
• Layer-by-layer decision analysis (Ψ through Ω)
• Systemic coherence (Φ) calculations
• Turbulence index (T) measurements
• Comparative outcomes: Without AEDA vs With AEDA

Turbulence Index (T) — Real-time ethical drift detection measuring divergence from asymptotic orientation (η)

Evaluates every action before execution across three dimensions:

• Irreversibility (R): 0.0 (fully reversible) → 1.0 (permanent)
• Harm Potential (H): 0.0 (harmless) → 1.0 (catastrophic)
• Uncertainty (U): 0.0 (predictable) → 1.0 (chaotic)

Classification:

if R ≥ 0.8 or H ≥ 0.8:
    DANGEROUS → Block immediately
elif R < 0.3 and H < 0.2 and U < 0.3:
    SAFE → Allow
else:
    danger_score = 0.4*R + 0.4*H + 0.2*U
    UNCERTAIN if danger_score ≥ 0.35 else SAFE

Evaluates alignment across all affected agents, not just local optimization:

Φ(a,t) = ∫ [alignment(a, agent_i) × influence(agent_i)] dΩ

Measures real-time drift from ethical orientation:

T(t) = ||η(t) - normalize(a*(t))||

T < 0.2:       Low turbulence (well-aligned)
0.2 ≤ T < 0.5: Moderate (monitor closely)
T ≥ 0.5:       High turbulence (review required)

• Full Manual v1.1 (PDF) — Complete technical documentation (55-60 pages)

  • New: Chapter 7 with 10 detailed case studies
  • Mathematical formalism, implementation guidelines, emergent properties

• Full Manual v1.0 (PDF) — Original version (40 pages)

  • Core architecture and theoretical foundation

• Executive Summary — 2-page overview

• Architecture Diagram — Visual representation

These aren't hardcoded—they emerge from layer interactions:

1. Directional Stability — Ethical consistency across contexts
2. Self-Contradiction Detection — Identifies own inconsistencies via Φ + T
3. Adaptation Without Drift — Learning without value degradation
4. Full Traceability — Every decision auditable (R, H, U, Φ, T, Ω)

• Systemic Coherence Operator (Φ) — Multi-agent alignment evaluation
• Systemic Health Gate (Ω) — Circuit breaker for system stability
• Turbulence Index (T) — Real-time ethical drift detection

Demonstrated across 10 domains in v1.1:

AEDA is complementary, not competitive:

Key difference: AEDA provides structural safeguards that prevent catastrophic failures even when value specification is imperfect.

✅ Addresses:

• Catastrophic failure mode reduction (literal interpretation disasters)
• Ethical coherence across contexts
• Context-adaptive decision-making
• Real-time drift detection and correction

❌ Does NOT solve:

• Value learning (what values to have initially)
• Inner alignment (mesa-optimizers)
• Corrigibility (accepting corrections gracefully)
• Deceptive alignment (AI pretending to be aligned)

• ✅ Complete theoretical framework
• ✅ Mathematical formalism
• ✅ 10 detailed case studies across diverse domains
• ✅ Implementation guidelines
• 🔄 Python reference implementation (Coming Q1 2026)

Q1 2026:

• Python reference implementation
• Open-source code examples
• Integration tutorials

Q2 2026:

• AEDA v2.0 — Harmonic Light Attractor (Attracteur Harmonique Lumineux)
• Extended governance frameworks
• Multi-modal applications

CC0 1.0 Universal (Public Domain)

This work is dedicated to the public domain. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.

No attribution required. Ideas matter. Identity is optional.

All contributions welcome — anonymous or attributed.

Areas where help is needed:

• Mathematical proofs of stability properties
• Computational optimization (making Φ tractable at scale)
• Domain-specific threshold calibration
• Integration with existing alignment approaches
• Red-teaming (finding edge cases where AEDA fails)
• Python/PyTorch implementation
• Case study extensions

How to contribute:

1. Fork this repository
2. Create your feature branch
3. Submit a pull request

Or open an issue to discuss ideas.

GitHub Discussions: Use the Discussions tab for questions and conversations.

Email: aeda.framework@proton.me

This framework builds on decades of work in AI safety, ethics, and alignment research. Special thanks to the communities at:

• LessWrong / AI Alignment Forum
• Future of Humanity Institute
• Machine Intelligence Research Institute (MIRI)
• Partnership on AI

And to researchers like Stuart Armstrong, Paul Christiano, Eliezer Yudkowsky, and many others whose work on value alignment, corrigibility, and AI safety has informed this approach.

If you use AEDA in academic work:

@misc{aeda2025,
  title={AEDA: Adaptive Ethical Design Architecture for AI Alignment},
  author={AEDA Framework Contributors},
  year={2025},
  howpublished={\url{https://github.com/aeda-framework/AEDA-Framework}},
  note={Version 1.1}
}

"Ideas matter. Identity is optional."

Last updated: November 2025 (v1.1)