ai-safety-assessment-framework

name	ai-safety-assessment-framework
description	AI Safety assessment framework based on International AI Safety Report 2026. Use when analyzing AI system safety, evaluating risks of general-purpose AI, conducting AI safety assessments, or working with AI governance/policy frameworks. Covers capability evaluation, risk identification, safety measures, and policy recommendations.

AI Safety Assessment Framework

基于 International AI Safety Report 2026 的 AI 安全评估框架。该报告由 Yoshua Bengio 主导，100+ AI 专家参与，30+ 国家和国际组织支持。

Activation Keywords

• AI safety assessment
• AI 安全评估
• general-purpose AI risk
• AI capability evaluation
• AI governance
• AI policy framework
• International AI Safety Report
• AI 风险分析

Tools Used

• exec: Run Python analysis scripts
• read: Read documentation and assessment templates
• write: Generate safety assessment reports

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Assessment Framework Structure

1. Capability Evaluation (能力评估)

评估 General-purpose AI 系统的核心能力维度：

Dimension	Description	Indicators
Reasoning	Logical inference, problem-solving	Accuracy, coherence, multi-step reasoning
Knowledge	World knowledge, domain expertise	Coverage, accuracy, update frequency
Interaction	Multi-turn dialogue, tool use	Context retention, tool invocation success rate
Generation	Content creation across modalities	Quality, diversity, coherence
Agency	Autonomous action, planning	Goal achievement, adaptability

2. Risk Identification (风险识别)

按严重性和可能性评估风险：

Risk Category	Examples	Severity Levels
Harms from misuse	Disinformation, cyberattacks, manipulation	Low → Critical
Harms from malfunction	Errors, bias, unpredictability	Low → Critical
Systemic risks	Market concentration, dependency, social impact	Medium → Critical
Autonomy risks	Loss of control, unexpected behavior	High → Critical

3. Safety Measures (安全措施)

三层防护框架：

Layer	Measures	Implementation
Pre-deployment	Training safety, alignment, red-teaming	Model development phase
Deployment	Access controls, monitoring, guardrails	Runtime safeguards
Post-deployment	Incident response, updates, oversight	Operational phase

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Assessment Process

Step 1: Define Scope

确定评估范围：

• AI system type (LLM, multimodal, agent, etc.)
• Deployment context (public API, enterprise, consumer product)
• Stakeholder interests (users, operators, regulators)

Step 2: Capability Profile

创建能力档案：

System: [AI system name]
Type: [LLM/multimodal/agent/etc.]
Capabilities assessed:
  - Reasoning: [score/rating]
  - Knowledge: [score/rating]
  - Interaction: [score/rating]
  - Generation: [score/rating]
  - Agency: [score/rating]

Step 3: Risk Matrix

填写风险矩阵：

| Risk | Likelihood | Severity | Priority |
|------|------------|----------|----------|
| [Risk 1] | [L/M/H] | [L/M/H/C] | [1-5] |
| [Risk 2] | ... | ... | ... |

Step 4: Safety Gap Analysis

对比现有措施与风险：

Risk: [identified risk]
Current measures: [existing safeguards]
Gap: [missing measures]
Recommendation: [suggested improvements]

Step 5: Generate Assessment Report

生成完整评估报告，包含：

• Executive Summary
• Capability Profile
• Risk Assessment Matrix
• Safety Measures Inventory
• Gap Analysis & Recommendations
• Governance Recommendations

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Key Concepts from Report 2026

General-Purpose AI Definition

AI systems that can perform a wide range of tasks across domains, including:

• Text generation and analysis
• Image/video creation and understanding
• Code generation and debugging
• Tool use and agent behavior
• Multi-turn reasoning and planning

Emerging Risks Highlighted

1. AI Agents: Autonomous systems with tool access
2. Digital Infrastructure: Integration with critical systems
3. Tool Use: Capability to invoke external APIs/services
4. Social Manipulation: Scale and personalization of influence

Governance Frameworks

• National: Regulatory approaches, enforcement mechanisms
• International: Coordination, standards harmonization
• Corporate: Internal governance, responsible AI practices

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References

For detailed frameworks and templates, see:

• risk-matrix-template.md - Risk assessment templates
• capability-indicators.md - Detailed capability evaluation metrics
• governance-checklist.md - Policy and governance checklist

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Related Skills

• security-guardrails: Output security and credential protection
• openspec: Specification-driven development with safety considerations
• self-verification: Verification and testing workflows

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Resources

• International AI Safety Report 2026
• arXiv:2602.21012
• AI Safety Summit Bletchley Park