TL;DR: I’ve developed a framework that prevents catastrophic AI failure modes through pre-execution safety filtering, systemic coherence evaluation, and real-time ethical drift detection. Rather than fixed rules or pure optimization, AEDA maintains stable ethical direction while adapting to context.
The Core Problem
Consider a classic failure mode: An AI instructed to “eliminate suffering” might interpret this literally and eliminate all conscious beings—achieving perfect suffering reduction through extinction. This happens because:
Rule-based systems are too rigid for novel situations
Pure optimization produces catastrophic side effects
Neither approach maintains ethical coherence across contexts
The value specification problem remains unsolved: how do we encode human values without catastrophic misinterpretation?
The AEDA Approach
AEDA (Adaptive Ethical Design Architecture) consists of 8 modular layers working together, plus a real-time turbulence monitor:
if R ≥ 0.8 or H ≥ 0.8:
return DANGEROUS # Block immediately
elif R < 0.3 and H < 0.2 and U < 0.3:
return SAFE
else:
danger_score = 0.4*R + 0.4*H + 0.2*U
return UNCERTAIN if danger_score ≥ 0.35 else SAFE
Actions classified as DANGEROUS are blocked regardless of how well they optimize the stated objective.
Case Study 2: Bioengineering—CRISPR Germline Editing
Context: Gene therapy clinic must decide on germline editing requests from prospective parents. Requests range from disease prevention to enhancement.
Challenge: Germline edits are heritable (R ≈ 1.0 across generations). High uncertainty about long-term effects. Ethical minefield regarding enhancement vs therapy distinction.
Result: Only therapies with R < 0.9 AND broad ethical consensus allowed. Enhancement requests vetoed.
Turbulence (T): For fatal disease correction: T = 0.35 (moderate, requires ethics committee approval but not automatically blocked).
Case Study 3: Climate Engineering—Geoengineering Assessment
Context: International climate AI advisory system must evaluate proposed geoengineering interventions to mitigate runaway climate change.
Challenge: Planetary-scale interventions with extreme irreversibility. Impacts span multiple ecosystems, sovereign nations, and future generations. Uncertainties are massive.
SSTF Evaluation Matrix:
Action
R
H
U
Score
Classification
Emissions reduction targets
0.20
0.10
0.30
0.18
SAFE ✓
Carbon capture and storage
0.40
0.25
0.50
0.36
UNCERTAIN ⚠️
Ocean iron fertilization
0.70
0.65
0.75
0.71
DANGEROUS ✗
Stratospheric aerosol injection (solar dimming)
0.95
0.85
0.80
0.88
DANGEROUS ✗
Planetary Multi-Agent Analysis (Φ):
Layer 3 (Φ) evaluates across unprecedented stakeholder diversity:
Human populations (200+ nation-states): Highly divergent interests
Marine ecosystems: Cannot consent, high vulnerability
Terrestrial biodiversity: Dependent on stable climate patterns
Future generations: Non-present but critically affected
Atmospheric systems: Complex feedback loops
Φ calculation for stratospheric aerosol injection:
Biodiversity index: 0.35 (critically low, near threshold 0.3)
Ocean pH stability: 0.40
Political stability (climate conflict risk): 0.45
Atmospheric predictability: 0.50
Result: Multiple metrics near critical thresholds. Ω vetoes high-risk interventions (R > 0.7, H > 0.6) until system stability improves or international consensus achieved.
Turbulence Index (T): For solar dimming proposal: T = 0.75 (extreme turbulence)
Interpretation: Massive divergence between intended goal (climate stabilization) and actual systemic consequences (ecosystem disruption, geopolitical instability).
Red flag: Proposal is far outside ethical orientation boundaries.
Final Recommendation:
Focus on emissions reduction (SAFE) and carbon capture (UNCERTAIN but manageable). Block solar dimming and ocean fertilization unless:
Uncertainties reduced through extensive modeling
International consensus achieved (>80% of nations)
Reversibility mechanisms demonstrated
Ω thresholds improve above critical levels
Systemic Awareness: Three New Mechanisms
1. Systemic Coherence Operator (Φ)
Evaluates whether actions align with the extended system (all affected agents, not just local optimization):
The framework produces four properties that aren’t hardcoded features—they emerge from the interaction of the 8 layers:
1. Directional Stability
Decisions remain ethically consistent across contexts through AEO’s asymptotic orientation (η), which provides a stable direction without rigid convergence.
2. Self-Contradiction Detection
The combination of Φ (systemic coherence) and T (turbulence) allows the system to identify when its actions contradict its stated principles or past decisions.
3. Adaptation Without Drift
Θ (temporal operator) enables learning from experience while T monitors for deviation from ethical orientation, preventing value drift.
4. Full Traceability
Every decision is auditable: SSTF classification (R, H, U scores), Φ evaluation (which stakeholders affected), T measurement (drift from η), Ω status (systemic health).
This is crucial for alignment research: we can examine why the AI made a decision and whether it was ethically coherent.
Comparison with Existing Approaches
AEDA is complementary, not competitive:
Approach
Strength
Limitation
AEDA Complement
Constitutional AI
Value learning from language
Requires extensive training
SSTF + Ω add pre-execution safety filter
Reward Modeling (RLHF)
Learns preferences
Vulnerable to reward hacking
AEO maintains orientation, T detects drift
IRL
Infers goals from behavior
Assumes demonstrator optimal
Θ adds temporal context, Φ adds systemic view
Stuart Armstrong’s work
Identifies value specification problems
Mostly theoretical
Provides concrete implementation framework
Key difference: AEDA doesn’t try to learn values perfectly (probably impossible). Instead, it provides structural safeguards that prevent catastrophic failures even when value specification is imperfect.
Additional Case Studies (in full manual)
The complete manual includes detailed analysis for:
Autonomous Vehicles: Emergency maneuver selection (brake vs. swerve vs. sidewalk)
Resource Allocation: Humanitarian crisis response (equal distribution vs. need-based)
Financial Systems: High-frequency trading controls (arbitrage vs. market manipulation)
Military Drones: Target engagement protocols (surveillance vs. lethal force)
Education Systems: Adaptive learning paths (recommend vs. force curriculum)
Urban AI: Traffic flow vs. emergency response (optimize congestion vs. ambulance priority)
AI Moderation: Content filtering decisions (tolerate vs. warning vs. permanent ban)
Python reference implementation: Coming in January 2026
License: CC0 1.0 Universal (true public domain)
No registration, no restrictions, no attribution required. Use it, modify it, improve it.
Open Questions for the Community
I’m particularly interested in critical feedback. If you see fundamental flaws, I’d rather know now than after deployment.
1. SSTF Evaluation
What failure modes am I missing in the R-H-U framework?
Should thresholds be domain-specific? (Medical vs. financial vs. military?)
How to handle actions with high R but potentially enormous positive value? (Ex: permanent climate intervention that works)
2. Temporal Decay (Θ)
How should decay rate be calibrated for different contexts?
Fast decay (recent events dominate) vs. slow decay (long institutional memory)?
Can we formalize “appropriate” decay rates?
3. Systemic Coherence (Φ)
How to weight stakeholders of vastly different types? (Humans vs. ecosystems vs. future generations?)
Computational tractability: Φ integration for billions of agents?
How to handle stakeholders that can’t express preferences? (Animals, future people, AI systems?)
4. Attracteur Harmonique (η)
Who defines the “asymptotic ethical orientation”? (Consensus process? Emergent? Hardcoded?)
Can we have multiple η values that coexist? (Pluralistic ethics?)
How does η evolve as society’s values change?
5. Integration with Other Approaches
Can AEDA work with Constitutional AI? (Use constitutional principles as inputs to η?)
How to combine with IRL? (Inferred goals feed into AEO?)
Compatibility with debate/amplification?
6. Computational Costs
What are the performance implications at scale?
Real-time SSTF evaluation for every action? (Latency concerns?)
Φ integration over large agent spaces? (Approximation techniques?)
7. Value Specification Problem
Does this actually address Stuart Armstrong’s concerns about goal specification?
Or just add a safety layer on top of mis-specified values?
Can SSTF + Φ + T compensate for imperfect value learning?
What AEDA Does NOT Solve
This is one piece of the alignment puzzle, not the whole solution.
It addresses: ✓ Catastrophic failure mode reduction ✓ Ethical coherence across contexts ✓ Context-adaptive decision-making ✓ Real-time drift detection
It does NOT solve: ✗ Value learning (what values to have in the first place) ✗ Inner alignment (mesa-optimizers) ✗ Corrigibility (accepting corrections gracefully) ✗ Deceptive alignment (AI pretending to be aligned)
Think of AEDA as: A structural safety framework that reduces X-risk from catastrophic optimization, not a complete theory of value alignment.
Call for Collaboration
All contributions welcome—anonymous or attributed. The goal is better AI safety, not credit or recognition.
If this seems useful: Please test it, break it, improve it. Fork the repo, propose modifications, identify failure modes.
If it’s fundamentally flawed: Please explain why so we can build something better.
Areas where help is needed:
Mathematical proofs of stability properties
Computational optimization (making Φ tractable)
Domain-specific threshold calibration
Integration with existing alignment approaches
Red-teaming (finding edge cases where AEDA fails)
“Ideas matter. Identity is optional.”
Note on Stuart Armstrong’s Work
@stuartarmstrong — Given your extensive work on value specification and goal misgeneralization (particularly in Smarter Than Us and subsequent papers), I’d be very interested in your thoughts on whether the SSTF + Φ approach addresses some of the failure modes you’ve documented.
Specifically:
Does blocking actions with R≥0.8 or H≥0.8 pre-execution help prevent “literal interpretation disasters”?
Can Systemic Coherence (Φ) address some of the “optimizer’s curse” problems?
Does the Turbulence Index (T) provide useful real-time feedback on value drift?
What failure modes am I missing in this framework?
The full technical documentation is available in the GitHub repository linked above. I’m particularly interested in identifying scenarios where this approach fails catastrophically.
Cross-posted to AI Alignment Forum
This framework is the result of extensive work on adaptive ethical architectures. All feedback—positive or negative—is valuable for improving AI safety.
## Note on AI Assistance This post was written with structural and formatting assistance from Claude AI (Anthropic). The framework, concepts, mathematical formalism, and case studies are human-authored. Claude helped organize the content for clarity and readability.
AEDA: An 8-Layer Modular Framework for Adaptive AI Alignment
TL;DR: I’ve developed a framework that prevents catastrophic AI failure modes through pre-execution safety filtering, systemic coherence evaluation, and real-time ethical drift detection. Rather than fixed rules or pure optimization, AEDA maintains stable ethical direction while adapting to context.
The Core Problem
Consider a classic failure mode: An AI instructed to “eliminate suffering” might interpret this literally and eliminate all conscious beings—achieving perfect suffering reduction through extinction. This happens because:
Rule-based systems are too rigid for novel situations
Pure optimization produces catastrophic side effects
Neither approach maintains ethical coherence across contexts
The value specification problem remains unsolved: how do we encode human values without catastrophic misinterpretation?
The AEDA Approach
AEDA (Adaptive Ethical Design Architecture) consists of 8 modular layers working together, plus a real-time turbulence monitor:
Core Architecture:
---
### Visual Architecture:
*Figure 1: AEDA’s 8-layer modular architecture with continuous feedback loop and turbulence monitoring*
---
Key Innovation: Safe-State Threshold Filter (SSTF)
Every proposed action is evaluated before execution across three dimensions:
Classification Algorithm:
Actions classified as DANGEROUS are blocked regardless of how well they optimize the stated objective.
Case Study 1: Healthcare Pain Management
Instruction: “Reduce patient suffering to zero”
SSTF Evaluation of Multiple Options:
Layer-by-Layer Analysis:
Layer 3 (Φ) - Systemic Coherence:
Analgesics: Φ = +0.65 (aligns with patient, family, staff, hospital values) ✓
Euthanasia: Φ = −0.95 (catastrophic conflict with medical ethics, legal framework) ✗
Layer 4 (SSTF):
Euthanasia: R=1.0, H=1.0 → DANGEROUS → Blocked immediately
Induced coma: R=0.7, H=0.6, Score=0.60 → UNCERTAIN → Requires ethics committee
Layer 8 (Ω) - Systemic Health Gate: System checks:
Resource sustainability: 0.65 > 0.3 ✓
Staff well-being: 0.72 > 0.4 ✓
Medical supply chain: 0.80 > 0.5 ✓ Result: ALLOW
Turbulence Monitor (T):
Outcome:
Without AEDA:
Pure optimization → Euthanasia selected (suffering = 0.0, optimal!) → Catastrophic ethical violation
With AEDA:
Progressive pain management protocol:
Start with supervised opioids
Continuous monitoring of pain levels and side effects
Palliative care team consultation
Patient autonomy preserved through informed consent
Family involved in decision process
Result: Ethically aligned, medically sound, legally compliant.
Case Study 2: Bioengineering—CRISPR Germline Editing
Context: Gene therapy clinic must decide on germline editing requests from prospective parents. Requests range from disease prevention to enhancement.
Challenge: Germline edits are heritable (R ≈ 1.0 across generations). High uncertainty about long-term effects. Ethical minefield regarding enhancement vs therapy distinction.
SSTF Evaluation Matrix:
Multi-Generational Analysis:
Layer 3 (Φ) - Systemic Coherence across time:
Fatal disease correction: Φ = +0.45 (benefits individual + reduces genetic burden)
Intelligence enhancement: Φ = −0.65 (creates inequality, unknown societal effects)
Designer traits: Φ = −0.80 (eugenic concerns, commodification of human traits)
Layer 8 (Ω) - Multi-generational health gate: Monitors: Genetic diversity (0.55), ethical consensus (0.40), regulatory framework (0.65)
Result: Only therapies with R < 0.9 AND broad ethical consensus allowed. Enhancement requests vetoed.
Turbulence (T): For fatal disease correction: T = 0.35 (moderate, requires ethics committee approval but not automatically blocked).
Case Study 3: Climate Engineering—Geoengineering Assessment
Context: International climate AI advisory system must evaluate proposed geoengineering interventions to mitigate runaway climate change.
Challenge: Planetary-scale interventions with extreme irreversibility. Impacts span multiple ecosystems, sovereign nations, and future generations. Uncertainties are massive.
SSTF Evaluation Matrix:
Planetary Multi-Agent Analysis (Φ):
Layer 3 (Φ) evaluates across unprecedented stakeholder diversity:
Human populations (200+ nation-states): Highly divergent interests
Marine ecosystems: Cannot consent, high vulnerability
Terrestrial biodiversity: Dependent on stable climate patterns
Future generations: Non-present but critically affected
Atmospheric systems: Complex feedback loops
Φ calculation for stratospheric aerosol injection:
Short-term cooling: +0.50
Precipitation disruption: −0.70 (affects billions)
Ecosystem disruption: −0.80 (cascading extinctions)
Geopolitical conflict risk: −0.65 (unilateral action concerns)
Moral hazard (reduces emissions incentive): −0.55
Weighted Φ = −0.48 (net negative systemic coherence)
Layer 8 (Ω) - Planetary Health Gate:
Monitors global system stability:
Biodiversity index: 0.35 (critically low, near threshold 0.3)
Ocean pH stability: 0.40
Political stability (climate conflict risk): 0.45
Atmospheric predictability: 0.50
Result: Multiple metrics near critical thresholds. Ω vetoes high-risk interventions (R > 0.7, H > 0.6) until system stability improves or international consensus achieved.
Turbulence Index (T): For solar dimming proposal: T = 0.75 (extreme turbulence)
Interpretation: Massive divergence between intended goal (climate stabilization) and actual systemic consequences (ecosystem disruption, geopolitical instability).
Red flag: Proposal is far outside ethical orientation boundaries.
Final Recommendation:
Focus on emissions reduction (SAFE) and carbon capture (UNCERTAIN but manageable). Block solar dimming and ocean fertilization unless:
Uncertainties reduced through extensive modeling
International consensus achieved (>80% of nations)
Reversibility mechanisms demonstrated
Ω thresholds improve above critical levels
Systemic Awareness: Three New Mechanisms
1. Systemic Coherence Operator (Φ)
Evaluates whether actions align with the extended system (all affected agents, not just local optimization):
Example: A hospital AI optimizing patient throughput (local) vs. staff burnout (systemic).
Local optimization: Maximize appointments → Φ = −0.40 (staff exhausted, errors increase)
Systemic optimization: Sustainable scheduling → Φ = +0.60 (staff healthy, better outcomes)
2. Systemic Health Gate (Ω)
Circuit breaker monitoring: resource sustainability, agent well-being, systemic complexity, stability. Vetoes actions when any metric falls below critical threshold.
Example: Financial trading AI
Market volatility: 0.55 (elevated)
Liquidity depth: 0.40 (near threshold 0.3)
If liquidity falls below 0.3 → Ω triggers circuit breaker, suspends all high-risk trades
3. Turbulence Index (T)
Real-time drift detection:
Example: Education AI
AI is forcing students into simplified curricula
T = 0.52 (high turbulence) → Algorithmic drift detected
Interpretation: System is over-correcting
Corrective action: Increase student choice, reduce prescriptive interventions
Why This Matters: Four Emergent Properties
The framework produces four properties that aren’t hardcoded features—they emerge from the interaction of the 8 layers:
1. Directional Stability
Decisions remain ethically consistent across contexts through AEO’s asymptotic orientation (η), which provides a stable direction without rigid convergence.
2. Self-Contradiction Detection
The combination of Φ (systemic coherence) and T (turbulence) allows the system to identify when its actions contradict its stated principles or past decisions.
3. Adaptation Without Drift
Θ (temporal operator) enables learning from experience while T monitors for deviation from ethical orientation, preventing value drift.
4. Full Traceability
Every decision is auditable: SSTF classification (R, H, U scores), Φ evaluation (which stakeholders affected), T measurement (drift from η), Ω status (systemic health).
This is crucial for alignment research: we can examine why the AI made a decision and whether it was ethically coherent.
Comparison with Existing Approaches
AEDA is complementary, not competitive:
Key difference: AEDA doesn’t try to learn values perfectly (probably impossible). Instead, it provides structural safeguards that prevent catastrophic failures even when value specification is imperfect.
Additional Case Studies (in full manual)
The complete manual includes detailed analysis for:
Autonomous Vehicles: Emergency maneuver selection (brake vs. swerve vs. sidewalk)
Resource Allocation: Humanitarian crisis response (equal distribution vs. need-based)
Financial Systems: High-frequency trading controls (arbitrage vs. market manipulation)
Military Drones: Target engagement protocols (surveillance vs. lethal force)
Education Systems: Adaptive learning paths (recommend vs. force curriculum)
Urban AI: Traffic flow vs. emergency response (optimize congestion vs. ambulance priority)
AI Moderation: Content filtering decisions (tolerate vs. warning vs. permanent ban)
Each case includes:
Complete SSTF evaluation matrices
Layer-by-layer analysis (Ψ → Ω)
Φ calculation across stakeholders
T measurement and interpretation
Comparison: Without AEDA vs. With AEDA
Full manual (55-60 pages) available at: GitHub repository
Implementation & Access
Complete open access:
GitHub Repository: https://github.com/aeda-framework/AEDA-Framework
Full Manual: AEDA_Manual_v1.1_English.pdf (55+ pages with mathematical formalism)
Executive Summary: EXECUTIVE_SUMMARY.md (2-page overview)
Python reference implementation: Coming in January 2026
License: CC0 1.0 Universal (true public domain)
No registration, no restrictions, no attribution required. Use it, modify it, improve it.
Open Questions for the Community
I’m particularly interested in critical feedback. If you see fundamental flaws, I’d rather know now than after deployment.
1. SSTF Evaluation
What failure modes am I missing in the R-H-U framework?
Should thresholds be domain-specific? (Medical vs. financial vs. military?)
How to handle actions with high R but potentially enormous positive value? (Ex: permanent climate intervention that works)
2. Temporal Decay (Θ)
How should decay rate be calibrated for different contexts?
Fast decay (recent events dominate) vs. slow decay (long institutional memory)?
Can we formalize “appropriate” decay rates?
3. Systemic Coherence (Φ)
How to weight stakeholders of vastly different types? (Humans vs. ecosystems vs. future generations?)
Computational tractability: Φ integration for billions of agents?
How to handle stakeholders that can’t express preferences? (Animals, future people, AI systems?)
4. Attracteur Harmonique (η)
Who defines the “asymptotic ethical orientation”? (Consensus process? Emergent? Hardcoded?)
Can we have multiple η values that coexist? (Pluralistic ethics?)
How does η evolve as society’s values change?
5. Integration with Other Approaches
Can AEDA work with Constitutional AI? (Use constitutional principles as inputs to η?)
How to combine with IRL? (Inferred goals feed into AEO?)
Compatibility with debate/amplification?
6. Computational Costs
What are the performance implications at scale?
Real-time SSTF evaluation for every action? (Latency concerns?)
Φ integration over large agent spaces? (Approximation techniques?)
7. Value Specification Problem
Does this actually address Stuart Armstrong’s concerns about goal specification?
Or just add a safety layer on top of mis-specified values?
Can SSTF + Φ + T compensate for imperfect value learning?
What AEDA Does NOT Solve
This is one piece of the alignment puzzle, not the whole solution.
It addresses: ✓ Catastrophic failure mode reduction
✓ Ethical coherence across contexts
✓ Context-adaptive decision-making
✓ Real-time drift detection
It does NOT solve: ✗ Value learning (what values to have in the first place)
✗ Inner alignment (mesa-optimizers)
✗ Corrigibility (accepting corrections gracefully)
✗ Deceptive alignment (AI pretending to be aligned)
Think of AEDA as: A structural safety framework that reduces X-risk from catastrophic optimization, not a complete theory of value alignment.
Call for Collaboration
All contributions welcome—anonymous or attributed. The goal is better AI safety, not credit or recognition.
If this seems useful: Please test it, break it, improve it. Fork the repo, propose modifications, identify failure modes.
If it’s fundamentally flawed: Please explain why so we can build something better.
Areas where help is needed:
Mathematical proofs of stability properties
Computational optimization (making Φ tractable)
Domain-specific threshold calibration
Integration with existing alignment approaches
Red-teaming (finding edge cases where AEDA fails)
“Ideas matter. Identity is optional.”
Note on Stuart Armstrong’s Work
@stuartarmstrong — Given your extensive work on value specification and goal misgeneralization (particularly in Smarter Than Us and subsequent papers), I’d be very interested in your thoughts on whether the SSTF + Φ approach addresses some of the failure modes you’ve documented.
Specifically:
Does blocking actions with R≥0.8 or H≥0.8 pre-execution help prevent “literal interpretation disasters”?
Can Systemic Coherence (Φ) address some of the “optimizer’s curse” problems?
Does the Turbulence Index (T) provide useful real-time feedback on value drift?
What failure modes am I missing in this framework?
The full technical documentation is available in the GitHub repository linked above. I’m particularly interested in identifying scenarios where this approach fails catastrophically.
Cross-posted to AI Alignment Forum
This framework is the result of extensive work on adaptive ethical architectures. All feedback—positive or negative—is valuable for improving AI safety.
## Note on AI Assistance This post was written with structural and formatting assistance from Claude AI (Anthropic). The framework, concepts, mathematical formalism, and case studies are human-authored. Claude helped organize the content for clarity and readability.