Quick Start

Create a basic responsible scaling policy structure:

YAML
risk_assessment:
  capability_thresholds:
    - level: "basic"
      indicators: ["task completion", "reasoning depth"]
      safeguards: ["human oversight", "output filtering"]
    - level: "advanced" 
      indicators: ["autonomous planning", "persuasion ability"]
      safeguards: ["enhanced monitoring", "deployment restrictions"]

evaluation_process:
  safety_cases:
    - evidence_requirements: ["red team results", "capability benchmarks"]
    - review_cycle: "pre-training, pre-deployment"
  
governance:
  internal: ["safety committee", "risk assessment team"]
  external: ["expert advisors", "regulatory engagement"]

Workflow

• Define capability thresholds that trigger safety upgrades
• Establish clear risk categories (catastrophic, high-impact, standard)
• Map safeguards to each risk level
• Create evaluation criteria for safeguard adequacy

• Develop safety case templates
• Define evidence requirements for each threshold
• Establish red team protocols
• Create capability measurement benchmarks

• Form internal safety committees
• Identify external expert advisors
• Create review and approval processes
• Establish accountability mechanisms

• Set deployment gates tied to safety assessments
• Create monitoring systems for deployed models
• Establish incident response protocols
• Design feedback loops for policy updates

Examples

Example 1: Capability Threshold Definition Input: Need thresholds for language model safety Output:

Threshold 1 (Basic): Coherent multi-turn conversation, factual Q&A
- Safeguards: Content filtering, usage monitoring
- Gates: Internal testing only

Threshold 2 (Intermediate): Creative writing, basic code generation
- Safeguards: Human review, restricted deployment
- Gates: Limited external beta

Threshold 3 (Advanced): Autonomous task planning, persuasive writing
- Safeguards: Enhanced monitoring, external review
- Gates: Full safety case required

Example 2: Safety Case Template Input: Framework for deployment decision Output:

Safety Case Requirements:
1. Capability Assessment: Benchmark results, red team findings
2. Risk Analysis: Potential misuse scenarios, failure modes
3. Safeguard Validation: Effectiveness testing, coverage analysis
4. Deployment Plan: Monitoring strategy, rollback procedures
5. External Review: Expert feedback, regulatory alignment

Best Practices

Flexible Thresholds: Use multiple indicators, not single metrics. Include qualitative assessments alongside quantitative benchmarks.

Iterative Improvement: Build in regular policy updates based on implementation experience and emerging risks.

Multi-Stakeholder Input: Engage technical experts, ethicists, policymakers, and affected communities in policy development.

Transparency Balance: Share methodology and principles while protecting sensitive technical details.

Cross-Industry Learning: Adapt proven risk management practices from nuclear, aviation, and pharmaceutical industries.

Precautionary Principle: Default to more restrictive safeguards when uncertainty is high.

Common Pitfalls

Static Policies: Creating rigid frameworks that can't adapt to rapid AI advancement or new risk discoveries.

Threshold Gaming: Setting capability thresholds that can be easily circumvented or gamed by developers.

Safeguard Theater: Implementing impressive-sounding but ineffective safety measures that don't actually reduce risk.

Internal Capture: Relying solely on internal teams without meaningful external oversight and input.

Binary Thinking: Treating safety as pass/fail rather than a continuous risk management challenge.

Implementation Gaps: Creating detailed policies on paper but failing to enforce them in practice during development pressure.