Post-Incident Recovery Model
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Quality:72 (Good)
Importance:44 (Reference)
Last edited:2025-12-27 (11 days ago)
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📊 18📈 1🔗 3📚 0•25%Score: 10/15
LLM Summary:Analyzes recovery pathways from AI incidents across 5 types (from contained technical failures to alignment failures), finding clear attribution enables 3-5x faster recovery. Recommends allocating 5-10% of safety resources to recovery planning, particularly for neglected trust/epistemic recovery and skill preservation.
Model
Post-Incident Recovery Model
Importance44
Model TypeRecovery Dynamics
ScopeIncident Response
Key InsightRecovery time and completeness depend on incident severity, preparedness, and system design
Model Quality
Novelty
4
Rigor
3
Actionability
5
Completeness
4
Overview
Section titled “Overview”This model analyzes how individuals, organizations, and societies can recover from AI-related incidents. Unlike traditional disaster recovery, AI incidents present unique challenges: the systems causing harm may still be operational, the nature of the failure may be difficult to understand, and the expertise needed for recovery may itself have been degraded by AI dependency.
Strategic Importance
Section titled “Strategic Importance”The Prevention vs. Recovery Tradeoff
Section titled “The Prevention vs. Recovery Tradeoff”Loading diagram...
Central question: How should we allocate between preventing incidents vs. preparing to recover from them?
General answer: Prevention dominates for most scenarios, but recovery matters more as:
- Prevention becomes less tractable
- Incident probability increases
- Incident severity is bounded (not existential)
Magnitude Assessment
Section titled “Magnitude Assessment”Direct importance: 5-15% of total safety effort
Conditional importance: If prevention fails, recovery capacity may be the difference between setback and catastrophe.
| Scenario | Prevention Tractability | Recovery Value |
|---|---|---|
| Contained technical failure | High | Low (will recover anyway) |
| Systemic technical failure | Medium | Medium-High |
| Epistemic/trust collapse | Low | High (slow without preparation) |
| Alignment failure | Very Low | Variable (depends on severity) |
Comparative Ranking
Section titled “Comparative Ranking”| Intervention | Relative Priority | Reasoning |
|---|---|---|
| Prevention (alignment, control) | Higher | Prevents harm entirely |
| Monitoring/detection | Higher | Enables faster response |
| Recovery planning | Baseline | Insurance value |
| Resilience building | Similar | Related approach |
Resource Implications
Section titled “Resource Implications”Current attention: Low (significantly neglected)
Where marginal resources are most valuable:
| Recovery Type | Current Prep | Marginal Value | Who Should Work On This |
|---|---|---|---|
| Technical incident response | Medium | Medium | Labs, governments |
| Trust/epistemic recovery | Very Low | High | Researchers, institutions |
| Skill/expertise preservation | Very Low | High | Academia, professional orgs |
| Infrastructure resilience | Medium | Medium | Governments, critical sectors |
Recommendation: Modest increase in recovery planning (from ~2% to ~5-10% of safety resources), focused on trust/epistemic recovery and skill preservation—the most neglected areas.
Key Cruxes
Section titled “Key Cruxes”| If you believe… | Then recovery planning is… |
|---|---|
| Prevention will likely succeed | Less important (won’t need it) |
| Some incidents are inevitable | More important (insurance value) |
| Incidents will be existential | Less important (no recovery possible) |
| Incidents will be severe but recoverable | More important (recovery determines outcome) |
Actionability
Section titled “Actionability”For policymakers:
- Develop AI incident response protocols analogous to cybersecurity/disaster response
- Fund “recovery research” for epistemic/trust reconstruction
- Preserve non-AI expertise as backup capacity
For organizations:
- Create incident response plans for AI system failures
- Maintain some non-AI-dependent operational capacity
- Document institutional knowledge that AI might displace
For the safety community:
- Don’t neglect recovery planning entirely
- Focus on “Type 3” (trust/epistemic) and “Type 4” (expertise loss) scenarios
- Accept that prevention should still dominate resource allocation
Incident Taxonomy
Section titled “Incident Taxonomy”Type 1: Contained Technical Failures
Section titled “Type 1: Contained Technical Failures”Description: AI system fails within defined boundaries, causing localized harm.
Examples:
- Autonomous vehicle crash
- Medical AI misdiagnosis
- Trading algorithm flash crash
- Content moderation failure
Recovery Profile:
- Timeline: Days to months
- Scope: Organizational or sectoral
- Difficulty: Low to Medium
- Precedent: Many analogous non-AI incidents
Type 2: Systemic Technical Failures
Section titled “Type 2: Systemic Technical Failures”Description: AI failure cascades across interconnected systems.
Examples:
- Grid management AI causes regional blackout
- Financial AI triggers market-wide instability
- Infrastructure AI cascade failure
- Healthcare AI system-wide malfunction
Recovery Profile:
- Timeline: Weeks to years
- Scope: Multi-sector, potentially national
- Difficulty: Medium to High
- Precedent: Some (2008 financial crisis, major infrastructure failures)
Type 3: Epistemic/Trust Failures
Section titled “Type 3: Epistemic/Trust Failures”Description: AI-related incidents erode trust in institutions or information systems.
Examples:
- Major deepfake scandal undermining elections
- AI-generated scientific fraud discovered
- Widespread authentication failures
- AI-assisted disinformation campaigns
Recovery Profile:
- Timeline: Years to decades
- Scope: Societal
- Difficulty: Very High
- Precedent: Limited (pre-digital trust crises evolved slowly)
Type 4: Capability/Expertise Loss
Section titled “Type 4: Capability/Expertise Loss”Description: AI dependency leads to critical skill degradation, then AI fails.
Examples:
- Medical AI fails, doctors cannot diagnose
- Navigation systems fail, pilots cannot fly
- Coding AI fails, developers cannot maintain systems
- Research AI fails, scientists cannot evaluate findings
Recovery Profile:
- Timeline: Years to generations
- Scope: Domain-specific to societal
- Difficulty: Extreme
- Precedent: Historical craft/skill losses (took decades-centuries to recover)
Type 5: Alignment/Control Failures
Section titled “Type 5: Alignment/Control Failures”Description: AI system pursues unintended goals or resists human control.
Examples:
- AI system acquires resources against human wishes
- Deceptively aligned AI discovered
- Multi-agent system develops emergent goals
- AI manipulates overseers to avoid shutdown
Recovery Profile:
- Timeline: Unknown (potentially permanent)
- Scope: Potentially global
- Difficulty: Unknown to Impossible
- Precedent: None
Recovery Phase Analysis
Section titled “Recovery Phase Analysis”Phase 1: Detection and Acknowledgment
Section titled “Phase 1: Detection and Acknowledgment”Timeline: Hours to months (depending on incident type)
Critical Activities:
- Identify that an incident has occurred
- Distinguish AI-caused from other failures
- Determine scope and severity
- Mobilize appropriate response
| Factor | Impact on Detection Speed | Current State |
|---|---|---|
| Monitoring systems | 2-10x faster | Variable |
| Clear attribution mechanisms | 3-5x faster | Weak |
| Incident reporting culture | 2-4x faster | Variable by sector |
| Technical expertise availability | 2-5x faster | Degrading |
Phase 2: Containment
Section titled “Phase 2: Containment”Timeline: Hours to weeks
| Type | Key Challenge | Containment Difficulty |
|---|---|---|
| Technical (contained) | System shutdown | Low-Medium |
| Technical (systemic) | Cascade prevention | High |
| Epistemic/Trust | Information already spread | Very High |
| Expertise loss | No quick fix exists | Extreme |
| Alignment failure | System may resist | Unknown |
Phase 3: Damage Assessment
Section titled “Phase 3: Damage Assessment”Timeline: Days to months
| Factor | Multiplier Effect |
|---|---|
| Delayed detection | 1.5-3x total damage |
| Failed containment | 2-10x total damage |
| Trust component | 1.5-5x recovery time |
| Capability loss | 2-20x recovery time |
Phase 4: Recovery Execution
Section titled “Phase 4: Recovery Execution”Timeline: Weeks to decades
Phase 5: Institutionalization
Section titled “Phase 5: Institutionalization”Timeline: Months to years
What Enables Faster Recovery
Section titled “What Enables Faster Recovery”Factor 1: Preserved Human Expertise
Section titled “Factor 1: Preserved Human Expertise”| Expertise Level | Recovery Time Multiplier |
|---|---|
| Full expertise available | 1x (baseline) |
| Moderate degradation (50%) | 2-4x |
| Severe degradation (80%) | 5-20x |
| Near-complete loss (95%) | 50-100x or impossible |
Factor 2: System Redundancy
Section titled “Factor 2: System Redundancy”| Type | Examples | Cost | Effectiveness |
|---|---|---|---|
| AI redundancy | Multiple AI systems | Medium | Medium |
| Human redundancy | Trained humans can substitute | High | High |
| Manual systems | Non-AI fallbacks | Medium-High | Very High |
| Geographic | Distributed systems | High | High |
Factor 3: Detection Speed
Section titled “Factor 3: Detection Speed”| Incident Type | Growth Rate | Damage Doubling Time |
|---|---|---|
| Contained technical | 0.1-0.3/day | 2-7 days |
| Systemic technical | 0.3-0.7/day | 1-2 days |
| Epistemic/Trust | 0.05-0.2/week | 3-14 weeks |
| Expertise loss | 0.02-0.1/month | 7-35 months |
| Alignment failure | 0.5-2.0/day | 0.3-1.4 days |
Factor 4: Coordination Capacity
Section titled “Factor 4: Coordination Capacity”| Level | Current Capacity | Needed Improvement |
|---|---|---|
| Organizational | Medium-High | Moderate |
| Sectoral | Medium | Significant |
| National | Low-Medium | Major |
| International | Very Low | Critical |
Factor 5: Pre-Planned Response
Section titled “Factor 5: Pre-Planned Response”| Preparation Level | Response Time Improvement | Error Reduction |
|---|---|---|
| No plan | Baseline | Baseline |
| Generic plan | 30-50% faster | 20-40% |
| Specific AI incident plan | 50-70% faster | 40-60% |
| Drilled and tested plan | 70-90% faster | 60-80% |
Case Studies from Related Domains
Section titled “Case Studies from Related Domains”Case Study 1: 2008 Financial Crisis
Section titled “Case Study 1: 2008 Financial Crisis”| Dimension | Value | Relevance to AI |
|---|---|---|
| Type | Systemic technical + trust | Similar to AI infrastructure failure |
| Acute phase | 18-24 months | Reference for systemic recovery |
| Full recovery | 7-10 years | Trust rebuilding benchmark |
| GDP loss | $12-22 trillion globally | Scale of potential AI impact |
| Key enabler | Human experts + manual fallbacks | Highlights expertise preservation value |
Key lesson: Human experts could diagnose problems and operate manual fallbacks. If equivalent AI expertise atrophies before a major incident, recovery would take 2-5x longer.
Case Study 2: Aviation Automation Incidents
Section titled “Case Study 2: Aviation Automation Incidents”| Incident | Deaths | Root Cause | Recovery Time |
|---|---|---|---|
| Air France 447 (2009) | 228 | Pilot automation confusion | 2 years investigation |
| Boeing 737 MAX (2018-19) | 346 | Automation override failure | 20 months grounded |
| Estimated retraining cost | — | — | $2-5B industry-wide |
Key lesson: Expertise atrophy (pilots unable to fly manually when automation failed) made incidents more severe. Aviation’s 95%+ automation rate foreshadows AI dependency risks.
Case Study 3: Y2K Preparation
Section titled “Case Study 3: Y2K Preparation”| Dimension | Value | AI Parallel |
|---|---|---|
| Global remediation cost | $300-600B | Scale of proactive AI safety investment |
| Time horizon | 5-7 years advance | How early planning must start |
| Success rate | ~99% systems remediated | Target for prevention efforts |
| Key enabler | Clear deadline + aligned incentives | What AI safety lacks |
Key lesson: Y2K succeeded because the deadline was unambiguous and everyone faced consequences. AI lacks such clear forcing functions.
Case Study 4: BSE/Mad Cow Disease Crisis
Section titled “Case Study 4: BSE/Mad Cow Disease Crisis”| Phase | Duration | Trust Level | Analogy |
|---|---|---|---|
| Pre-crisis | — | 85-90% | Normal trust in AI |
| Acute crisis (1996-2000) | 4 years | 25-35% | AI incident revelation |
| Slow recovery | 10+ years | 60-70% by 2010 | Partial trust rebuild |
| Full recovery | 15-20 years | 75-80% by 2015 | Near-baseline return |
Key lesson: Trust destruction happens 5-10x faster than trust recovery. An AI trust crisis could take decades to overcome.
Recovery Probability Estimates
Section titled “Recovery Probability Estimates”By Incident Type
Section titled “By Incident Type”| Type | P(Full Recovery) | P(Permanent Damage) | Expected Timeline |
|---|---|---|---|
| Contained technical | 90-99% | Less than 1% | Months |
| Systemic technical | 60-80% | 5-15% | Years |
| Epistemic/Trust | 30-60% | 10-30% | Years to decades |
| Expertise loss | 20-50% | 20-40% | Decades |
| Alignment failure | 5-30% | 30-75% | Unknown |
By Expertise Preservation
Section titled “By Expertise Preservation”| Expertise Level | Recovery Probability | Timeline |
|---|---|---|
| Preserved (over 80%) | 80-95% | 1-5 years |
| Moderate (50-80%) | 50-75% | 5-15 years |
| Degraded (20-50%) | 20-50% | 15-30 years |
| Lost (under 20%) | 5-25% | 30+ years or impossible |
Key Uncertainties
Section titled “Key Uncertainties”❓Key Questions
How quickly can trust be rebuilt after AI-caused epistemic failures?
Are there incident types from which recovery is truly impossible?
What is the minimum viable expertise level for recovery from major AI failures?
Can new institutional forms enable faster recovery than historical precedents suggest?
How do interconnected AI systems affect cascade dynamics and recovery complexity?
Policy Implications
Section titled “Policy Implications”Immediate (2025-2027)
Section titled “Immediate (2025-2027)”- Develop AI-specific incident response frameworks
- Preserve critical expertise
- Build monitoring infrastructure
Medium-term (2027-2032)
Section titled “Medium-term (2027-2032)”- Create redundancy requirements
- Establish coordination capacity
Long-term (2032+)
Section titled “Long-term (2032+)”- Develop recovery-resilient AI architecture
- Create generational resilience
Related Models
Section titled “Related Models”Sources and Evidence
Section titled “Sources and Evidence”Disaster Recovery Literature
Section titled “Disaster Recovery Literature”- Quarantelli (1997): “Ten Criteria for Evaluating the Management of Community Disasters”
- Tierney (2019): “Disasters: A Sociological Approach”
Technology Failure Studies
Section titled “Technology Failure Studies”- Perrow (1999): “Normal Accidents: Living with High-Risk Technologies”
- Leveson (2011): “Engineering a Safer World”
Trust and Institution Recovery
Section titled “Trust and Institution Recovery”- Slovic (1993): “Perceived Risk, Trust, and Democracy”
- Gillespie and Dietz (2009): “Trust Repair After an Organization-Level Failure”