Global Resilience
Why Resilience?
Section titled “Why Resilience?”Most AI safety work focuses on prevention: ensuring AI systems don’t cause harm. But prevention may fail. Resilience asks: If things go wrong, how do we limit damage and recover?
Resilience is valuable because:
- Uncertainty: We may not prevent all AI harms
- Redundancy: Defense in depth is wise
- Graceful degradation: Partial failures shouldn’t become total failures
- Recovery capacity: Even after harm, rebuilding matters
Key Resilience Domains
Section titled “Key Resilience Domains”Epistemic Resilience
Section titled “Epistemic Resilience”Maintaining society’s ability to know what’s true despite AI-enabled deception:
- Epistemic Security — Protecting collective knowledge and truth-finding capacity
- Content Authentication — Verifying what’s real in a synthetic content era
- Institutional Trust — Preserving and rebuilding trust in key institutions
Infrastructure Resilience
Section titled “Infrastructure Resilience”Ensuring critical systems function despite AI-related disruption:
- Critical Infrastructure Protection — Power, communications, finance, healthcare
- AI Dependency Management — Avoiding single points of AI failure
- Cyber Resilience — Defending against AI-enhanced cyber attacks
Social Resilience
Section titled “Social Resilience”Maintaining social cohesion and function under AI-induced stress:
- Economic Adaptation — Managing AI-driven labor disruption
- Democratic Resilience — Protecting democratic processes from AI manipulation
- Community Resilience — Local capacity for mutual aid and recovery
Governance Resilience
Section titled “Governance Resilience”Ensuring governance systems remain functional and legitimate:
- Regulatory Adaptability — Governance that can respond to rapid AI change
- International Stability — Avoiding AI-triggered conflict escalation
- Institutional Redundancy — Backup systems for critical governance functions
Resilience Principles
Section titled “Resilience Principles”1. Redundancy
Section titled “1. Redundancy”No single point of failure. Multiple systems can perform critical functions.
2. Diversity
Section titled “2. Diversity”Avoid monoculture. Different approaches reduce correlated failures.
3. Modularity
Section titled “3. Modularity”Failures should be contained. Damage to one component shouldn’t cascade.
4. Graceful Degradation
Section titled “4. Graceful Degradation”Systems should fail partially, not totally. Reduced function beats no function.
5. Adaptability
Section titled “5. Adaptability”Systems should learn and adjust. Rigid systems break; flexible systems bend.
6. Recovery Capacity
Section titled “6. Recovery Capacity”After failure, systems should be rebuildable. Preserve knowledge and capacity for reconstruction.
The Resilience vs. Prevention Tradeoff
Section titled “The Resilience vs. Prevention Tradeoff”| Prevention Focus | Resilience Focus |
|---|---|
| Stop bad outcomes from occurring | Survive and recover if bad outcomes occur |
| Requires accurate prediction | Robust to prediction failure |
| High value if successful | Valuable even if prevention succeeds |
| May create fragility (single strategy) | Builds robustness (multiple defenses) |
Best approach: Both. Prevention is primary; resilience is backup.
Connection to AI Risk
Section titled “Connection to AI Risk”Why AI Safety Needs Resilience
Section titled “Why AI Safety Needs Resilience”| AI Risk | Resilience Relevance |
|---|---|
| Misalignment | May not be preventable; need to survive initial failures |
| Misuse | Can’t prevent all misuse; need to limit damage |
| Racing dynamics | May not be stoppable; need to handle fast development |
| Coordination failure | If coordination fails, resilience is fallback |
Resilience as Safety Complement
Section titled “Resilience as Safety Complement”Resilience isn’t a substitute for alignment or safety research. It’s a complement:
- Safety research: Make AI systems safe
- Resilience: Survive if safety fails
Neither alone is sufficient. Both together provide defense in depth.
Key Questions
Section titled “Key Questions”- How much should we invest in resilience vs. prevention?
- Which resilience measures are most tractable and important?
- How do we build resilience without creating new risks?
- What resilience measures are valuable across many AI scenarios?