Summary
- Anthropic shared recommendations for building reliable AI safety evaluations based on lessons from psychology and internal system cards.
- Strong safety evals require interdisciplinary teams combining technical methods experts with domain specialists like psychologists and biologists.
- Safety tests must run securely in sandboxed environments with real-time monitoring, especially for cyber or internet-enabled evals.
- Evaluations should match design to the underlying question, distinguishing between observational metrics and causal interventions.
- Construct validity is critical; metrics should measure appropriate multi-turn behavior, dual-use handling, and generalization rather than simple refusal rates.
Why it matters
Robust safety evaluations are foundational for responsible AI deployment, helping developers measure genuine risks rather than easily gamed proxies like simple refusal rates.