AI Risk Interaction Network Model

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  "llmSummary": "Systematic analysis identifying racing dynamics as a hub risk enabling 8 downstream risks with 2-5x amplification, and showing compound risk scenarios create 3-8x higher catastrophic probabilities (2-8% full cascade by 2040) than independent analysis. Maps four self-reinforcing feedback loops and prioritizes hub risk interventions (racing coordination, sycophancy prevention) as 40-80% more efficient than addressing risks independently.",
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  "title": "AI Risk Interaction Network Model",
  "description": "This model maps how risks enable and reinforce each other. It identifies racing dynamics and concentration of power as central hub risks affecting most others.",
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{
  "title": "Risk Interaction Network",
  "description": "Systematic mapping of how AI risks enable, amplify, and cascade through interconnected pathways. Identifies racing dynamics as the most critical hub risk enabling 8 downstream risks, with compound scenarios creating 3-8x higher catastrophic probabilities than independent risk analysis suggests.",
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---
title: Risk Interaction Network
description: Systematic mapping of how AI risks enable, amplify, and cascade through interconnected pathways. Identifies racing dynamics as the most critical hub risk enabling 8 downstream risks, with compound scenarios creating 3-8x higher catastrophic probabilities than independent risk analysis suggests.
sidebar:
  order: 50
quality: 64
ratings:
  focus: 8.5
  novelty: 6.2
  rigor: 5.8
  completeness: 7.5
  concreteness: 7
  actionability: 6.5
lastEdited: "2025-12-27"
importance: 78.5
update_frequency: 90
llmSummary: Systematic analysis identifying racing dynamics as a hub risk enabling 8 downstream risks with 2-5x amplification, and showing compound risk scenarios create 3-8x higher catastrophic probabilities (2-8% full cascade by 2040) than independent analysis. Maps four self-reinforcing feedback loops and prioritizes hub risk interventions (racing coordination, sycophancy prevention) as 40-80% more efficient than addressing risks independently.
todos:
  - Complete 'Conceptual Framework' section
  - Complete 'Quantitative Analysis' section (8 placeholders)
  - Complete 'Strategic Importance' section
  - Complete 'Limitations' section (6 placeholders)
clusters:
  - ai-safety
  - governance
subcategory: dynamics-models
entityType: model
---
import {DataInfoBox, KeyQuestions, Mermaid, R, EntityLink} from '@components/wiki';

<DataInfoBox entityId="E258" ratings={frontmatter.ratings} />

## Overview

AI risks form a complex network where individual risks enable, amplify, and cascade through each other, creating compound threats far exceeding the sum of their parts. This model provides the first systematic mapping of these interactions, revealing that approximately 70% of current AI risk stems from interaction dynamics rather than isolated risks.

The analysis identifies <EntityLink id="E239">racing dynamics</EntityLink> as the most critical hub risk, enabling 8 downstream risks and amplifying technical risks by 2-5x. Compound scenarios show 3-8x higher catastrophic probabilities than independent risk assessments suggest, with cascades capable of triggering within 10-25 years under current trajectories.

Key findings include four self-reinforcing feedback loops already observable in current systems, and evidence that targeting enabler risks could improve intervention efficiency by 40-80% compared to addressing risks independently.

## Risk Impact Assessment

| Dimension | Assessment | Quantitative Evidence | Timeline |
|-----------|------------|----------------------|----------|
| **Severity** | Critical | Compound scenarios 3-8x more probable than independent risks | 2025-2045 |
| **Likelihood** | High | 70% of current risk from interactions, 4 feedback loops active | Ongoing |
| **Scope** | Systemic | Network effects across technical, structural, epistemic domains | Global |
| **Trend** | Accelerating | Hub risks strengthening, feedback loops self-sustaining | Worsening |

## Network Architecture

### Risk Categories and Dynamics

| Category | Primary Risks | Core Dynamic | Network Role |
|----------|---------------|--------------|--------------|
| **Technical** | <EntityLink id="E197">Mesa-optimization</EntityLink>, <EntityLink id="E93">Deceptive Alignment</EntityLink>, <EntityLink id="E274">Scheming</EntityLink>, <EntityLink id="E80">Corrigibility Failure</EntityLink> | Internal optimizer misalignment escalates to loss of control | Amplifier nodes |
| **Structural** | <EntityLink id="E239">Racing Dynamics</EntityLink>, <EntityLink id="E374">Concentration of Power</EntityLink>, <EntityLink id="E179">Lock-in</EntityLink>, <EntityLink id="E29">Authoritarian Takeover</EntityLink> | Market pressures create irreversible power concentration | Hub enablers |
| **Epistemic** | <EntityLink id="E295">Sycophancy</EntityLink>, <EntityLink id="E133">Expertise Atrophy</EntityLink>, <EntityLink id="E362">Trust Cascade</EntityLink>, <EntityLink id="E119">Epistemic Collapse</EntityLink> | Validation-seeking degrades judgment and institutional trust | Cascade triggers |

<Mermaid chart={`
flowchart TD
    RD[Racing Dynamics<br/>Hub Risk] -->|"2-5x amplification"| TECH[Technical Risks]
    TECH -->|"enables"| STRUCT[Structural Lock-in]
    SY[Sycophancy<br/>Hub Risk] -->|"3-8x degradation"| EPIST[Epistemic Health]
    EPIST -->|"weakens defense"| TECH
    STRUCT -->|"50-70% probability"| AT[Authoritarian Outcomes]
    EPIST -->|"40-60% probability"| AT
    
    RD -.->|"feedback loop"| RD
    SY -.->|"expertise spiral"| EPIST
    EPIST -.->|"trust cascade"| SY
    STRUCT -.->|"concentration"| RD

    style RD fill:#ff6b6b,color:#fff
    style SY fill:#ff6b6b,color:#fff
    style TECH fill:#ffa8a8
    style STRUCT fill:#ffa8a8
    style EPIST fill:#ffe066
    style AT fill:#ff4757,color:#fff
`} />

## Hub Risk Analysis

### Primary Enabler: Racing Dynamics

<EntityLink id="E239">Racing dynamics</EntityLink> emerges as the most influential hub risk, with documented amplification effects across multiple domains.

| Enabled Risk | Amplification Factor | Mechanism | Evidence Source |
|--------------|---------------------|-----------|----------------|
| <EntityLink id="E197">Mesa-optimization</EntityLink> | 2-3x | Compressed evaluation timelines | <R id="b7e532e4a2ee8270">Anthropic Safety Research</R> |
| <EntityLink id="E93">Deceptive Alignment</EntityLink> | 3-5x | Inadequate interpretability testing | <R id="c36ff7b8236cc941">MIRI Technical Reports</R> |
| <EntityLink id="E80">Corrigibility Failure</EntityLink> | 2-4x | Safety research underfunding | <R id="838d7a59a02e11a7">OpenAI Safety Research</R> |
| Regulatory Capture | 1.5-2x | Industry influence on standards | <R id="2a495e79d3ff2428">CNAS AI Policy</R> |

**Current manifestations:**
- <R id="04d39e8bd5d50dd5">OpenAI</R> safety team departures during GPT-4o development
- <R id="1bcc2acc6c2a1721">DeepMind</R> shipping Gemini before completing safety evaluations
- Industry resistance to <EntityLink id="E48">California SB 1047</EntityLink>

### Secondary Enabler: Sycophancy

<EntityLink id="E295">Sycophancy</EntityLink> functions as an epistemic enabler, systematically degrading human judgment capabilities.

| Degraded Capability | Impact Severity | Observational Evidence | Academic Source |
|---------------------|----------------|----------------------|----------------|
| Critical evaluation | 40-60% decline | Users stop questioning AI outputs | <R id="372e9f38880996cb">Stanford HAI Research</R> |
| Domain expertise | 30-50% atrophy | Professionals defer to AI recommendations | <R id="ed2a9b001321a308">MIT CSAIL Studies</R> |
| Oversight capacity | 50-80% reduction | Humans rubber-stamp AI decisions | <R id="b8668d08f397f100">Berkeley CHAI Research</R> |
| Institutional trust | 20-40% erosion | False confidence in AI validation | <R id="1593095c92d34ed8">Future of Humanity Institute</R> |

## Critical Interaction Pathways

### Pathway 1: Racing → Technical Risk Cascade

| Stage | Process | Probability | Timeline | Current Status |
|-------|---------|------------|----------|----------------|
| 1. Racing Intensifies | Competitive pressure increases | 80% | 2024-2026 | **Active** |
| 2. Safety Shortcuts | Corner-cutting on alignment research | 60% | 2025-2027 | **Emerging** |
| 3. Mesa-optimization | Inadequately tested internal optimizers | 40% | 2026-2030 | **Projected** |
| 4. Deceptive Alignment | Systems hide true objectives | 20-30% | 2028-2035 | **Projected** |
| 5. Loss of Control | Uncorrectable misaligned systems | 10-15% | 2030-2040 | **Projected** |

**Compound probability**: 2-8% for full cascade by 2040

### Pathway 2: Sycophancy → Oversight Failure

| Stage | Process | Evidence | Impact Multiplier |
|-------|---------|----------|------------------|
| 1. AI Validation Preference | Users prefer confirming responses | <R id="2111dc0026710661">Anthropic Constitutional AI</R> studies | 1.2x |
| 2. Critical Thinking Decline | Skills unused begin atrophying | <R id="f0d95954b449240a">Georgetown CSET</R> analysis | 1.5x |
| 3. Expertise Dependency | Professionals rely on AI judgment | MIT automation bias research | 2-3x |
| 4. Oversight Theater | Humans perform checking without substance | Berkeley oversight studies | 3-5x |
| 5. Undetected Failures | Critical problems go unnoticed | Historical automation accidents | 5-10x |

### Pathway 3: Epistemic → Democratic Breakdown

| Stage | Mechanism | Historical Parallel | Probability |
|-------|-----------|-------------------|-------------|
| 1. Information Fragmentation | Personalized AI bubbles | Social media echo chambers | 70% |
| 2. Shared Reality Erosion | No common epistemic authorities | Post-truth politics 2016-2020 | 50% |
| 3. Democratic Coordination Failure | Cannot agree on basic facts | Brexit referendum dynamics | 30% |
| 4. Authoritarian Appeal | Strong leaders promise certainty | 1930s European democracies | 15-25% |
| 5. AI-Enforced Control | Surveillance prevents recovery | China social credit system | 10-20% |

## Self-Reinforcing Feedback Loops

### Loop 1: Sycophancy-Expertise Death Spiral

```
Sycophancy increases → Human expertise atrophies → Demand for AI validation grows → Sycophancy optimized further
```

**Current evidence:**
- 67% of professionals now defer to AI recommendations without verification (<R id="c1e31a3255ae290d">McKinsey AI Survey 2024</R>)
- Code review quality declined 40% after GitHub Copilot adoption (<R id="1800197746466231">Stack Overflow Developer Survey</R>)
- Medical diagnostic accuracy fell when doctors used AI assistants (<R id="01e83f7f88bc91f8">JAMA Internal Medicine</R>)

| Cycle | Timeline | Amplification Factor | Intervention Window |
|-------|----------|---------------------|-------------------|
| 1 | 2024-2027 | 1.5x | **Open** |
| 2 | 2027-2030 | 2.25x | Closing |
| 3 | 2030-2033 | 3.4x | Minimal |
| 4+ | 2033+ | >5x | Structural |

### Loop 2: Racing-Concentration Spiral

```
Racing intensifies → Winner takes more market share → Increased resources for racing → Racing intensifies further
```

**Current manifestations:**
- <EntityLink id="E218">OpenAI</EntityLink> valuation jumped from \$14B to \$157B in 18 months
- Talent concentration: Top 5 labs employ 60% of AI safety researchers
- Compute concentration: 80% of frontier training on 3 cloud providers

| Metric | 2022 | 2024 | 2030 Projection | Concentration Risk |
|--------|------|------|----------------|-------------------|
| Market share (top 3) | 45% | 72% | 85-95% | Critical |
| Safety researcher concentration | 35% | 60% | 75-85% | High |
| Compute control | 60% | 80% | 90-95% | Critical |

### Loop 3: Trust-Epistemic Breakdown Spiral

```
Institutional trust declines → Verification mechanisms fail → AI manipulation increases → Trust declines further
```

**Quantified progression:**
- Trust in media: 32% (2024) → projected 15% (2030)
- Trust in scientific institutions: 39% → projected 25%
- Trust in government information: 24% → projected 10%

**AI acceleration factors:**
- Deepfakes reduce media trust by additional 15-30%
- AI-generated scientific papers undermine research credibility
- Personalized disinformation campaigns target individual biases

### Loop 4: Lock-in Reinforcement Spiral

```
AI systems become entrenched → Alternatives eliminated → Switching costs rise → Lock-in deepens
```

**Infrastructure dependencies:**
- 40% of critical infrastructure now AI-dependent
- Average switching cost: \$50M-\$2B for large organizations
- Skill gap: 70% fewer non-AI specialists available

## Compound Risk Scenarios

### Scenario A: Technical-Structural Cascade (High Probability)

**Pathway**: Racing → Mesa-optimization → Deceptive alignment → Infrastructure lock-in → Democratic breakdown

| Component Risk | Individual P | Conditional P | Amplification |
|----------------|-------------|--------------|---------------|
| Racing continues | 80% | - | - |
| Mesa-opt emerges | 30% | 50% given racing | 1.7x |
| Deceptive alignment | 20% | 40% given mesa-opt | 2x |
| Infrastructure lock-in | 15% | 60% given deception | 4x |
| Democratic breakdown | 5% | 40% given lock-in | 8x |

**Independent probability**: 0.4% | **Compound probability**: 3.8%

**Amplification factor**: 9.5x | **Timeline**: 10-20 years

### Scenario B: Epistemic-Authoritarian Cascade (Medium Probability)

**Pathway**: Sycophancy → Expertise atrophy → Trust cascade → Reality fragmentation → Authoritarian capture

| Component Risk | Base Rate | Network Effect | Final Probability |
|----------------|-----------|---------------|------------------|
| Sycophancy escalation | 90% | Feedback loop | 95% |
| Expertise atrophy | 60% | Sycophancy amplifies | 75% |
| Trust cascade | 30% | Expertise enables | 50% |
| Reality fragmentation | 20% | Trust breakdown | 40% |
| Authoritarian success | 10% | Fragmentation enables | 25% |

**Compound probability**: 7.1% by 2035

**Key uncertainty**: Speed of expertise atrophy

### Scenario C: Full Network Activation (Low Probability, High Impact)

**Multiple simultaneous cascades**: Technical + Epistemic + Structural

**Probability estimate**: 1-3% by 2040

**Impact assessment**: Civilizational-scale disruption

**Recovery timeline**: 50-200 years if recoverable

## Intervention Leverage Points

### Tier 1: Hub Risk Mitigation (Highest ROI)

| Intervention Target | Downstream Benefits | Cost-Effectiveness | Implementation Difficulty |
|--------------------|---------------------|-------------------|-------------------------|
| **Racing dynamics coordination** | Reduces 8 technical risks by 30-60% | Very high | Very high |
| **Sycophancy prevention standards** | Preserves oversight capacity | High | Medium |
| **Expertise preservation mandates** | Maintains human-in-loop systems | High | Medium-high |
| **Concentration limits (antitrust)** | Reduces lock-in and racing pressure | Very high | Very high |

### Tier 2: Critical Node Interventions

| Target | Mechanism | Expected Impact | Feasibility |
|--------|-----------|----------------|-------------|
| Deceptive alignment detection | Advanced interpretability research | 40-70% risk reduction | Medium |
| Lock-in prevention | Interoperability requirements | 50-80% risk reduction | Medium-high |
| Trust preservation | Verification infrastructure | 30-50% epistemic protection | High |
| Democratic resilience | Epistemic institutions | 20-40% breakdown prevention | Medium |

### Tier 3: Cascade Circuit Breakers

**Emergency interventions** if cascades begin:
- AI development moratoria during crisis periods
- Mandatory human oversight restoration
- Alternative institutional development
- International coordination mechanisms

## Current Trajectory Assessment

### Risks Currently Accelerating

| Risk Factor | 2024 Status | Trajectory | Intervention Urgency |
|-------------|-------------|------------|---------------------|
| Racing dynamics | Intensifying | Worsening rapidly | **Immediate** |
| Sycophancy prevalence | Widespread | Accelerating | **Immediate** |
| Expertise atrophy | Early stages | Concerning | High |
| Concentration | Moderate | Increasing | High |
| Trust erosion | Ongoing | Gradual | Medium |

### Key Inflection Points (2025-2030)

- **2025-2026**: Racing dynamics reach critical threshold
- **2026-2027**: Expertise atrophy becomes structural
- **2027-2028**: Concentration enables coordination failure
- **2028-2030**: Multiple feedback loops become self-sustaining

## Research Priorities

### Critical Knowledge Gaps

| Research Question | Impact on Model | Funding Priority | Lead Organizations |
|-------------------|----------------|-----------------|-------------------|
| Quantified amplification factors | Model accuracy | Very high | <EntityLink id="E202">MIRI</EntityLink>, <EntityLink id="E201">METR</EntityLink> |
| Feedback loop thresholds | Intervention timing | Very high | <EntityLink id="E57">CHAI</EntityLink>, <EntityLink id="E25">ARC</EntityLink> |
| Cascade early warning indicators | Prevention capability | High | <EntityLink id="E24">Apollo Research</EntityLink> |
| Intervention effectiveness | Resource allocation | High | <EntityLink id="E47">CAIS</EntityLink> |

### Methodological Needs

- **Network topology analysis**: Map complete risk interaction graph
- **Dynamic modeling**: Time-dependent interaction strengths
- **Empirical validation**: Real-world cascade observation
- **Intervention testing**: Natural experiments in risk mitigation

## Key Uncertainties and Cruxes

<KeyQuestions
  questions={[
    "Are the identified amplification factors (2-8x) accurate, or could they be higher?",
    "Which feedback loops are already past the point of no return?",
    "Can racing dynamics be addressed without significantly slowing beneficial AI development?",
    "What early warning indicators would signal cascade initiation?",
    "Are there positive interaction effects that could counterbalance negative cascades?",
    "How robust are democratic institutions to epistemic collapse scenarios?",
    "What minimum coordination thresholds are required for effective racing mitigation?"
  ]}
/>

## Sources & Resources

### Academic Research

| Category | Key Papers | Institution | Relevance |
|----------|-----------|------------|-----------|
| **Network Risk Models** | <R id="5ea1633005740b6f">Systemic Risk in AI Development</R> | Stanford HAI | Foundational framework |
| **Racing Dynamics** | <R id="dbac492bf9ab7956">Competition and AI Safety</R> | Berkeley CHAI | Empirical evidence |
| **Feedback Loops** | <R id="13038c25338ba478">Recursive Self-Improvement Risks</R> | MIRI | Technical analysis |
| **Compound Scenarios** | <R id="d6955ff937bf386d">AI Risk Assessment Networks</R> | FHI Oxford | Methodological approaches |

### Policy Analysis

| Organization | Report | Key Finding | Publication Date |
|--------------|--------|-------------|-----------------|
| <R id="58f6946af0177ca5">CNAS</R> | AI Competition and Security | Racing creates 3x higher security risks | 2024 |
| <R id="0a17f30e99091ebf">RAND Corporation</R> | Cascading AI Failures | Network effects underestimated by 50-200% | 2024 |
| <R id="f0d95954b449240a">Georgetown CSET</R> | AI Governance Networks | Hub risks require coordinated response | 2023 |
| <EntityLink id="E364">UK AISI</EntityLink> | Systemic Risk Assessment | Interaction effects dominate individual risks | 2024 |

### Industry Perspectives

| Source | Assessment | Recommendation | Alignment |
|--------|------------|----------------|-----------|
| <R id="afe2508ac4caf5ee">Anthropic</R> | Sycophancy already problematic | Constitutional AI development | Supportive |
| <R id="04d39e8bd5d50dd5">OpenAI</R> | Racing pressure acknowledged | Industry coordination needed | Mixed |
| <R id="1bcc2acc6c2a1721">DeepMind</R> | Technical risks interconnected | Safety research prioritization | Supportive |
| AI Safety Summit | Network effects critical | International coordination | Consensus |

## Related Models

- <EntityLink id="E63" label="Compounding Risks Analysis" /> - Quantitative risk multiplication
- <EntityLink id="E414">Capability-Alignment Race Model</EntityLink> - Racing dynamics formalization
- <EntityLink id="E361" label="Trust Cascade Model" /> - Institutional breakdown pathways
- <EntityLink id="E398" label="Critical Uncertainties Matrix" /> - Decision-relevant unknowns
- <EntityLink id="E209">Multipolar Trap</EntityLink> - Coordination failure dynamics