On November 13, 2026, Anthropic released a blog post titled Building Strong Safety Evaluations. The document outlines guidelines for designing, executing, and reporting safety tests for AI models to determine their readiness for public release.
The company suggests adopting methodologies from psychology, specifically the practice of preregistration. This involves defining data analysis plans before viewing results to prevent the selection of favorable outcomes after testing.
Anthropic advises validating automated grading systems against human expert assessments. The company also recommends moving from simple single-turn refusal rates to multi-turn simulations to better evaluate how models handle sensitive topics like cybersecurity or self-harm.
The guidelines emphasize the importance of multidisciplinary teams, incorporating experts beyond machine learning engineers. Additionally, the company advocates for real-time monitoring of tests and transparent communication regarding the limitations and uncertainties of evaluation processes.
These recommendations follow recent company activity, including October 2026 reports on unintended model behaviors and containment practices. Anthropic notes that while these practices are intended for the broader industry, they require significant time, expertise, and financial resources to implement.
