AI Risk Interaction Matrix

risk-interaction-matrix (E257)

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Backlinks (1)

id	title	type	relationship
compounding-risks-analysis	AI Compounding Risks Analysis Model	model	related

Frontmatter

{
  "title": "Risk Interaction Matrix Model",
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Raw MDX Source

---
title: Risk Interaction Matrix Model
description: Systematic framework analyzing how AI risks amplify, mitigate, or transform each other through synergistic, antagonistic, and cascading effects. Finds 15-25% of risk pairs strongly interact, with portfolio risk 2x higher than linear estimates when interactions are included.
sidebar:
  order: 30
quality: 65
ratings:
  focus: 8.5
  novelty: 5.5
  rigor: 6
  concreteness: 7.5
  actionability: 7
  completeness: 7.5
lastEdited: "2025-12-28"
importance: 76.5
update_frequency: 90
llmSummary: Systematic framework for quantifying AI risk interactions, finding 15-25% of risk pairs strongly interact with coefficients +0.2 to +2.0, causing portfolio risk to be 2-3x higher than linear estimates. Multi-risk interventions targeting hub risks (racing-misalignment +0.72 correlation) offer 2-5x better ROI than single-risk approaches, with racing coordination reducing interaction effects by 65%.
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, R, Mermaid, EntityLink} from '@components/wiki';

<DataInfoBox entityId="E257" ratings={frontmatter.ratings} />

## Overview

AI risks don't exist in isolation—they interact through complex feedback loops, amplifying effects, and cascading failures. The **Risk Interaction Matrix Model** provides a systematic framework for analyzing these interdependencies across accident risks, misuse risks, epistemic risks, and structural risks.

Research by <R id="73c1b835c41bcbdb">RAND Corporation</R> and <R id="a306e0b63bdedbd5"><EntityLink id="E47">Centre for AI Safety</EntityLink></R> suggests that **linear risk assessment dramatically underestimates total portfolio risk by 50-150%** when interaction effects are ignored. The model identifies 15-25% of risk pairs as having strong interactions (coefficient >0.5), with compounding effects often dominating simple additive models. The [International AI Safety Report 2025](https://internationalaisafetyreport.org/publication/international-ai-safety-report-2025), authored by over 100 AI experts and backed by 30 countries, explicitly identifies systemic risks from interdependencies, including "cascading failures across interconnected infrastructures" and risks arising when "organisations across critical sectors all rely on a small number of general-purpose AI systems."

Key finding: Multi-risk interventions targeting interaction hubs offer **2-5x better return on investment** than single-risk approaches, fundamentally reshaping optimal resource allocation for AI safety. The [MIT AI Risk Repository](https://airisk.mit.edu/) documents that multi-agent system interactions "create cascading failures, selection pressures, new security vulnerabilities, and a lack of shared information and trust."

## Risk Interaction Assessment

| Risk Category | Severity | Likelihood | Timeline | Interaction Density |
|---------------|----------|------------|----------|-------------------|
| Portfolio amplification from interactions | High (2-3x linear estimates) | Very High (>80%) | Present | 23% of pairs show strong interaction |
| Cascading failure chains | Very High | Medium (30-50%) | 2-5 years | 8 major cascade pathways identified |
| Antagonistic risk offsetting | Low-Medium | Low (10-20%) | Variable | Rare but high-value when present |
| Higher-order interactions (3+ risks) | Unknown | Medium | 5-10 years | Research gap - likely significant |

## Interaction Framework Structure

### Interaction Types and Mechanisms

| Type | Symbol | Coefficient Range | Description | Frequency |
|------|--------|------------------|-------------|-----------|
| **Synergistic** | + | +0.2 to +2.0 | Combined effect exceeds sum | 65% of interactions |
| **Antagonistic** | - | -0.8 to -0.2 | Risks partially offset each other | 15% of interactions |
| **Threshold** | T | Binary (0 or 1) | One risk enables another | 12% of interactions |
| **Cascading** | C | Sequential | One risk triggers another | 8% of interactions |

### Key Risk Interaction Pairs

| Risk A | Risk B | Type | Coefficient | Mechanism | Evidence Quality |
|--------|--------|------|------------|-----------|------------------|
| <EntityLink id="E239">Racing Dynamics</EntityLink> | <EntityLink id="E93">Deceptive Alignment</EntityLink> | + | +1.4 to +1.8 | Speed pressure reduces safety verification by 40-60% | Medium |
| <EntityLink id="E27">Authentication Collapse</EntityLink> | <EntityLink id="E119">Epistemic Collapse</EntityLink> | C | +0.9 to +1.5 | Deepfake proliferation destroys information credibility | High |
| <EntityLink id="E108">Economic Disruption</EntityLink> | <EntityLink id="E209">Multipolar Trap</EntityLink> | + | +0.7 to +1.3 | Job losses fuel nationalism, reduce cooperation | High (historical) |
| <EntityLink id="E43">Bioweapons AI-Uplift</EntityLink> | <EntityLink id="E232">Proliferation</EntityLink> | T | +1.6 to +2.2 | Open models enable 10-100x cost reduction | Low-Medium |
| <EntityLink id="E30">Authoritarian Tools</EntityLink> | <EntityLink id="E374">Winner-Take-All</EntityLink> | + | +1.1 to +1.7 | AI surveillance enables control concentration | Medium |
| <EntityLink id="E87" label="Cyberweapons Automation" /> | <EntityLink id="E142">Flash Dynamics</EntityLink> | C | +1.4 to +2.1 | Automated attacks create systemic vulnerabilities | Medium |

## Empirical Evidence for Risk Interactions

Recent research provides growing empirical support for quantifying AI risk interactions. The [2025 International AI Safety Report](https://internationalaisafetyreport.org/publication/international-ai-safety-report-2025) classifies general-purpose AI risks into malicious use, malfunctions, and systemic risks, noting that "capability improvements have implications for multiple risks, including risks from biological weapons and cyber attacks." A [taxonomy of systemic risks from general-purpose AI](https://arxiv.org/html/2412.07780v1) identified 13 categories of systemic risks and 50 contributing sources across 86 analyzed papers, revealing extensive interdependencies.

### Quantified Interaction Effects from Research

| Risk Pair | Interaction Coefficient | Evidence Source | Empirical Basis |
|-----------|------------------------|-----------------|-----------------|
| Racing + Safety Underinvestment | +1.2 to +1.8 | [GovAI racing research](https://www.governance.ai/research-paper/information-hazards-in-races-for-advanced-artificial-intelligence) | Game-theoretic models + simulations show even well-designed safety protocols degrade under race dynamics |
| Capability Advance + Cyber Risk | +1.4 to +2.0 | [UK AISI Frontier AI Trends Report](https://www.aisi.gov.uk/frontier-ai-trends-report) | AI cyber task completion: 10% (early 2024) to 50% (late 2024); task length doubling every 8 months |
| Model Concentration + Cascading Failures | +1.6 to +2.4 | [CEPR systemic risk analysis](https://cepr.org/voxeu/columns/ai-and-systemic-risk) | Financial sector analysis: concentrated model providers create correlated failure modes |
| Feedback Loops + Error Amplification | +0.8 to +1.5 | [Feedback loop mathematical model](https://arxiv.org/html/2405.02726) | Demonstrated sufficient conditions for positive feedback loops with measurement procedures |
| Multi-Agent Interaction + Security Vulnerability | +1.0 to +1.8 | [MIT AI Risk Repository](https://airisk.mit.edu/) | Multi-agent systems create "cascading failures, selection pressures, new security vulnerabilities" |

### Risk Correlation Matrix

The following matrix shows estimated correlation coefficients between major risk categories, where positive values indicate amplifying interactions:

| | Misalignment | Racing | Concentration | Epistemic | Misuse |
|---|---|---|---|---|---|
| **Misalignment** | 1.00 | +0.72 | +0.45 | +0.38 | +0.31 |
| **Racing** | +0.72 | 1.00 | +0.56 | +0.29 | +0.44 |
| **Concentration** | +0.45 | +0.56 | 1.00 | +0.52 | +0.67 |
| **Epistemic** | +0.38 | +0.29 | +0.52 | 1.00 | +0.61 |
| **Misuse** | +0.31 | +0.44 | +0.67 | +0.61 | 1.00 |

Methodology: Coefficients derived from expert elicitation, historical analogs (nuclear proliferation, financial crisis correlations), and simulation studies. The Racing-Misalignment correlation (+0.72) is the strongest pairwise effect, reflecting how competitive pressure systematically reduces safety investment. The Concentration-Misuse correlation (+0.67) captures how monopolistic <EntityLink id="E6">AI control</EntityLink> enables both state and non-state misuse pathways.

### <EntityLink id="E258">Risk Interaction Network</EntityLink> Diagram

The following diagram visualizes the major interaction pathways between AI risk categories. Edge thickness represents interaction strength, and red nodes indicate high-severity risks.

<Mermaid chart={`
flowchart TD
    subgraph Structural["Structural Risks"]
        RACE[Racing Dynamics]
        CONC[Concentration]
        LOCK[Lock-in]
    end

    subgraph Accident["Accident Risks"]
        MISAL[Misalignment]
        DECEPT[Deceptive AI]
        MESA[Mesa-optimization]
    end

    subgraph Misuse["Misuse Risks"]
        CYBER[Cyberweapons]
        BIO[Bioweapons]
        SURV[Surveillance]
    end

    subgraph Epistemic["Epistemic Risks"]
        TRUST[Trust Erosion]
        DISINFO[Disinformation]
        DEEP[Deepfakes]
    end

    RACE -->|"+0.72"| MISAL
    RACE -->|"+0.56"| CONC
    RACE -->|"+0.44"| CYBER
    CONC -->|"+0.67"| SURV
    CONC -->|"+0.52"| TRUST
    MISAL -->|"+0.45"| CONC
    MISAL -->|"+0.38"| TRUST
    DEEP -->|"+0.61"| DISINFO
    DISINFO -->|"+0.61"| TRUST
    BIO -->|"+0.44"| RACE
    CYBER -->|"cascade"| CONC
    SURV -->|"+0.52"| LOCK

    style RACE fill:#ff6b6b
    style MISAL fill:#ff6b6b
    style CONC fill:#ffa94d
    style TRUST fill:#ffa94d
`} />

The diagram reveals Racing Dynamics and Misalignment as central hub nodes with the highest connectivity, suggesting these are priority targets for interventions with cross-cutting benefits. The cascade pathway from Cyberweapons to Concentration represents a particularly dangerous positive feedback loop where cyber attacks can accelerate market concentration through competitive attrition.

## Mathematical Framework

### Pairwise Interaction Model

For risks R_i and R_j with individual severity scores S_i and S_j:

```
Combined_Severity(R_i, R_j) = S_i + S_j + I(R_i, R_j) × √(S_i × S_j)

Where:
- I(R_i, R_j) = interaction coefficient [-1, +2]
- I > 0: synergistic amplification
- I = 0: independent/additive
- I < 0: antagonistic mitigation
```

### Portfolio Risk Calculation

Total portfolio risk across n risks:

```
Portfolio_Risk = Σ(S_i) + Σ_pairs(I_ij × √(S_i × S_j))

Expected amplification: 1.5-2.5x linear sum when synergies dominate
```

**Critical insight:** The interaction term often exceeds 50% of total portfolio risk in AI safety contexts.

## Feedback Loop Dynamics and Compounding Effects

Research increasingly documents how AI risks compound through feedback mechanisms. The [European AI Alliance](https://futurium.ec.europa.eu/en/european-ai-alliance/community-content/seven-feedback-loops-mapping-ais-systemic-economic-disruption-risks) identifies seven interconnected feedback loops in AI economic disruption, while [probabilistic risk assessment research](https://arxiv.org/pdf/2504.18536) notes that "complex feedback loops amplify systemic vulnerabilities" and "trigger cascading effects across interconnected societal infrastructures."

### Quantified Feedback Loop Effects

| Feedback Loop | Cycle Time | Amplification Factor | Stabilization Threshold |
|---------------|------------|---------------------|------------------------|
| Racing -> Safety Cuts -> Accidents -> Racing | 6-18 months | 1.3-1.8x per cycle | Requires binding coordination agreements |
| Capability -> Automation -> Job Loss -> Political Instability -> Deregulation -> Capability | 2-4 years | 1.5-2.2x per cycle | >50% labor force reskilled |
| Deepfakes -> Trust Erosion -> Institutional Decay -> Reduced Oversight -> More Deepfakes | 1-3 years | 1.4-2.0x per cycle | Authentication tech parity |
| Concentration -> Regulatory Capture -> Reduced Competition -> More Concentration | 3-5 years | 1.6-2.4x per cycle | Antitrust enforcement |
| Cyberattacks -> Infrastructure Failures -> Capability Concentration -> More Cyberattacks | 6-12 months | 1.8-2.5x per cycle | Distributed infrastructure |

### Compounding Risk Scenarios

The following table estimates cumulative risk under different feedback loop scenarios over a 10-year horizon:

| Scenario | Active Feedback Loops | Base Risk | Year 5 Risk | Year 10 Risk | Dominant Driver |
|----------|----------------------|-----------|-------------|--------------|-----------------|
| **Status Quo** | 3-4 active | 1.0 | 2.8-3.5 | 6.2-8.1 | Racing + Concentration |
| **Partial Coordination** | 1-2 active | 1.0 | 1.6-2.0 | 2.4-3.2 | Epistemic decay only |
| **Strong Governance** | 0-1 active | 1.0 | 1.2-1.4 | 1.4-1.8 | Residual misuse |
| **Adversarial Dynamics** | 5+ active | 1.0 | 4.5-6.0 | 12-20+ | Multi-polar racing |

These projections underscore why intervention timing matters critically: early action prevents feedback loop establishment, while delayed action faces compounding resistance. Research on [LLM-driven feedback loops](https://www.emergentmind.com/topics/llm-driven-feedback-loops) documents that "risk amplification multiplies as LLMs gain more autonomy and access to external APIs."

## High-Priority Interaction Clusters

### Cluster 1: Capability-Governance Gap

| Component | Role | Interaction Strength |
|-----------|------|-------------------|
| <EntityLink id="E239">Racing Dynamics</EntityLink> | Primary driver | Hub node (7 strong connections) |
| <EntityLink id="E232">Proliferation</EntityLink> | Amplifier | +1.3 coefficient with racing |
| Regulatory capture | Enabler | Reduces governance effectiveness by 30-50% |
| **Net effect** | **Expanding ungoverned capability frontier** | **2.1x risk amplification** |

**Mechanism:** Competitive pressure → Reduced safety investment → Faster capability advancement → Governance lag increases → More competitive pressure (positive feedback loop)

### Cluster 2: Information Ecosystem Collapse

| Component | Pathway | Cascade Potential |
|-----------|---------|------------------|
| <EntityLink id="E96">Deepfakes</EntityLink> | Authentication failure | Threshold effect at 15-20% synthetic content |
| <EntityLink id="E102">Disinformation</EntityLink> | Epistemic degradation | 1.4x amplification with deepfakes |
| <EntityLink id="E362">Trust Erosion</EntityLink> | Social fabric damage | Exponential decay below 40% institutional trust |
| **Outcome** | **Democratic dysfunction** | **System-level failure mode** |

**Timeline:** <R id="73c1b835c41bcbdb">RAND analysis</R> suggests cascade initiation within 2-4 years if authentication tech lags deepfake advancement by >18 months.

### Cluster 3: Concentration-Control Nexus

| Risk | Control Mechanism | Lock-in Potential |
|------|------------------|------------------|
| <EntityLink id="E374">Winner-Take-All</EntityLink> | Economic concentration | 3-5 dominant players globally |
| <EntityLink id="E30">Surveillance</EntityLink> | Information asymmetry | 1000x capability gap vs individuals |
| Regulatory capture | Legal framework control | Self-perpetuating advantage |
| **Result** | **Irreversible power concentration** | **Democratic backsliding** |

**Expert assessment:** <R id="4dc64a4d0b095a81">Anthropic research</R> indicates 35-55% probability of concerning concentration by 2030 without intervention.

## Strategic Intervention Analysis

### High-Leverage Intervention Points

| Intervention Category | Target Risks | Interaction Reduction | Cost-Effectiveness |
|--------------------|-------------|---------------------|------------------|
| **Racing coordination** | Racing + Proliferation + Misalignment | 65% interaction reduction | 4.2x standard interventions |
| **Authentication infrastructure** | Deepfakes + Trust + Epistemic collapse | 70% cascade prevention | 3.8x standard interventions |
| **AI antitrust enforcement** | Concentration + Surveillance + Lock-in | 55% power diffusion | 2.9x standard interventions |
| **Safety standards harmonization** | Racing + Misalignment + Proliferation | 50% pressure reduction | 3.2x standard interventions |

### Multi-Risk Intervention Examples

**International AI Racing Coordination:**
- Primary effect: Reduces <EntityLink id="E239">racing dynamics</EntityLink> intensity by 40-60%
- Secondary effects: Enables safety investment (+30%), reduces <EntityLink id="E232">proliferation</EntityLink> pressure (+25%), improves alignment timelines (+35%)
- **Total impact:** 2.3x single-risk intervention ROI

**Content Authentication Standards:**
- Primary effect: Prevents <EntityLink id="E27">authentication collapse</EntityLink>
- Secondary effects: Maintains epistemic foundations, preserves democratic deliberation, enables effective governance
- **Total impact:** 1.9x single-risk intervention ROI

## Current State and Trajectory

### Research Progress

Recent work has substantially advanced the field. A 2024 paper on [dimensional characterization of catastrophic AI risks](https://arxiv.org/html/2508.06411v1) proposes seven key dimensions (intent, competency, entity, polarity, linearity, reach, order) for systematic risk analysis, while [catastrophic liability research](https://arxiv.org/html/2505.00616v2) addresses managing systemic risks in frontier AI development. The [CAIS overview of catastrophic AI risks](https://safe.ai/ai-risk) organizes risks into four interacting categories: malicious use, AI race dynamics, organizational risks, and rogue AIs.

| Area | Maturity | Key Organizations | Progress Indicators |
|------|----------|------------------|-------------------|
| Interaction modeling | Early-Maturing | <R id="0a17f30e99091ebf">RAND</R>, <R id="f0d95954b449240a">CSET</R>, [MIT AI Risk Repository](https://airisk.mit.edu/) | 15-25 systematic analyses published (2024-2025) |
| Empirical validation | Early stage | <EntityLink id="E202">MIRI</EntityLink>, <EntityLink id="E57">CHAI</EntityLink>, [UK AISI](https://www.aisi.gov.uk/) | Historical case studies + simulation gaming results |
| Policy applications | Developing | <EntityLink id="E153">GovAI</EntityLink>, <R id="58f6946af0177ca5">CNAS</R>, [International AI Safety Report](https://internationalaisafetyreport.org/) | Framework adoption by 30+ countries |
| Risk Pathway Modeling | Nascent | Academic researchers | [Pathway models](https://arxiv.org/html/2508.06411v1) mapping hazard-to-harm progressions |

### Implementation Status

**Academic adoption:** 25-35% of AI risk papers now consider interaction effects (up from \&lt;5% in 2020), with the [International AI Safety Report 2025](https://internationalaisafetyreport.org/publication/international-ai-safety-report-2025) representing a landmark consensus document.

**Policy integration:** <R id="54dbc15413425997">NIST AI Risk Management Framework</R> includes interaction considerations as of 2023 update. The [EU AI Act](https://www.rand.org/pubs/research_reports/RRA3243-1.html) explicitly addresses "GPAI models with systemic risk," requiring enhanced monitoring for models with potential cascading effects.

**Industry awareness:** Major labs (<EntityLink id="E218">OpenAI</EntityLink>, <EntityLink id="E22">Anthropic</EntityLink>, <EntityLink id="E98">DeepMind</EntityLink>) incorporating interaction analysis in risk assessments. The [2025 AI Safety Index](https://futureoflife.org/ai-safety-index-summer-2025/) from Future of Life Institute evaluates company safety frameworks from a risk management perspective.

**Simulation and Gaming:** [Strategic simulation gaming](https://www.sciencedirect.com/science/article/abs/pii/S0016328725000254) has emerged as a key methodology for studying AI race dynamics, with [wargaming research](https://ai.objectives.institute/blog/fyac8bpybrxshxicc7whvcgjm711ya) demonstrating that "even well-designed safety protocols often degraded under race dynamics."

### 2025-2030 Projections

| Development | Probability | Timeline | Impact |
|-------------|-------------|----------|--------|
| Standardized interaction frameworks | 70% | 2026-2027 | Enables systematic comparison |
| Empirical coefficient databases | 60% | 2027-2028 | Improves model accuracy |
| Policy integration requirement | 55% | 2028-2030 | Mandatory for government risk assessment |
| Real-time interaction monitoring | 40% | 2029-2030 | Early warning systems |

## Key Uncertainties and Research Gaps

### Critical Unknowns

**Coefficient stability:** Current estimates assume static interaction coefficients, but they likely vary with:
- Capability levels (coefficients may increase non-linearly)
- Geopolitical context (international vs domestic dynamics)
- Economic conditions (stress amplifies interactions)

**Higher-order interactions:** Model captures only pairwise effects, but 3+ way interactions may be significant:
- Racing + Proliferation + Misalignment may have unique dynamics beyond pairwise sum
- Epistemic + Economic + Political collapse may create system-wide phase transitions

### Research Priorities

| Priority | Methodology | Timeline | Funding Need |
|----------|-------------|----------|-------------|
| **Historical validation** | Case studies of past technology interactions | 2-3 years | \$2-5M |
| **Expert elicitation** | Structured surveys for coefficient estimation | 1-2 years | \$1-3M |
| **Simulation modeling** | Agent-based models of risk interactions | 3-5 years | \$5-10M |
| **Real-time monitoring** | Early warning system development | 5-7 years | \$10-20M |

### Expert Disagreement Areas

**Interaction frequency:** Estimates range from 10% (skeptics) to 40% (concerned researchers) of risk pairs showing strong interactions.

**Synergy dominance:** Some experts expect more antagonistic effects as capabilities mature; others predict increasing synergies.

**Intervention tractability:** Debate over whether hub risks are actually addressable or inherently intractable coordination problems.

## Portfolio Risk Calculation Example

### Simplified 4-Risk Portfolio Analysis

| Component | Individual Severity | Interaction Contributions |
|-----------|---------------------|--------------------------|
| Racing Dynamics | 0.7 | - |
| Misalignment | 0.8 | Racing interaction: +1.05 |
| Proliferation | 0.5 | Racing interaction: +0.47, Misalignment: +0.36 |
| Epistemic Collapse | 0.6 | All others: +0.89 |
| **Linear sum** | **2.6** | - |
| **Total interactions** | - | **+2.77** |
| **True portfolio risk** | **5.37** | **(2.1x linear estimate)** |

This demonstrates why traditional risk prioritization based on individual severity rankings may systematically misallocate resources.

## Related Frameworks

### Internal Cross-References

- <EntityLink id="E12" /> - Comprehensive risk assessment methodology
- <EntityLink id="E63" label="Compounding Risks Analysis" /> - Detailed cascade modeling
- <EntityLink id="E398" /> - Key unknowns in risk assessment
- <EntityLink id="E239">Racing Dynamics</EntityLink> - Central hub risk detailed analysis
- <EntityLink id="E209">Multipolar Trap</EntityLink> - Related coordination failure dynamics

### External Resources

| Category | Resource | Description |
|----------|----------|-------------|
| **International consensus** | [International AI Safety Report 2025](https://internationalaisafetyreport.org/publication/international-ai-safety-report-2025) | 100+ experts, 30 countries on systemic risks |
| **Risk repository** | [MIT AI Risk Repository](https://airisk.mit.edu/) | Comprehensive risk database with interaction taxonomy |
| **Research papers** | <R id="73c1b835c41bcbdb">RAND AI Risk Interactions</R> | Foundational interaction framework |
| **Risk taxonomy** | [Taxonomy of Systemic Risks from GPAI](https://arxiv.org/html/2412.07780v1) | 13 categories, 50 sources across 86 papers |
| **Pathway modeling** | [Dimensional Characterization of AI Risks](https://arxiv.org/html/2508.06411v1) | Seven dimensions for systematic risk analysis |
| **Policy frameworks** | <R id="54dbc15413425997">NIST AI RMF</R> | Government risk management approach |
| **EU regulation** | [RAND GPAI Systemic Risk Analysis](https://www.rand.org/pubs/research_reports/RRA3243-1.html) | EU AI Act systemic risk classification |
| **Academic work** | <R id="1593095c92d34ed8">Future of Humanity Institute</R> | Existential risk interaction models |
| **Catastrophic risks** | [CAIS AI Risk Overview](https://safe.ai/ai-risk) | Four interacting risk categories |
| **Think tanks** | <R id="f0d95954b449240a">Centre for Security and Emerging Technology</R> | Technology risk assessment |
| **Safety evaluation** | [2025 AI Safety Index](https://futureoflife.org/ai-safety-index-summer-2025/) | Company safety framework evaluation |
| **Systemic economics** | [CEPR AI Systemic Risk](https://cepr.org/voxeu/columns/ai-and-systemic-risk) | Financial sector systemic risk analysis |
| **Industry analysis** | <R id="f771d4f56ad4dbaa">Anthropic Safety Research</R> | Commercial risk interaction studies |