Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems. Founded by former OpenAI researchers Dario and Daniela Amodei, Anthropic has emerged as a leading force in AI evaluation testing, particularly for safety, alignment, and responsible AI development.
Constitutional AI provides one answer by giving language models explicit values determined by a constitution, rather than values determined implicitly via large-scale human feedback. This methodology enables systematic evaluation of AI behavior against defined principles.
Process Overview:
The risk governance framework we use to mitigate potential catastrophic risks from frontier AI systems with specific evaluation triggers:
AI Safety Levels (ASL):
New AI models go through a wide range of safety evaluations—for example, testing their capacity to assist in the creation of biological or chemical weapons
Sabotage Evaluations:
Standard Benchmarks:
Advanced Evaluations:
We use the Bias Benchmark for QA (BBQ), an evaluation that tests for social biases against people belonging to protected classes along nine social dimensions
Anthropic has agreed to let the U.S. AI Safety Institute test their new models before releasing them to the public
Key Partnerships:
We have participated in third-party safety evaluations conducted by the Alignment Research Center (ARC), which assesses frontier AI models for dangerous capabilities
Implementing BBQ was more difficult than we anticipated. We could not find a working open-source implementation of BBQ that we could simply use "off the shelf"
HELM gives a misleading impression of Claude's performance due to format compatibility issues with Claude's Human/Assistant training approach.
Providing full-time assistance diverted resources from internal evaluation efforts when supporting external audits.
Constitutional AI (CAI), a method in which we replace human red teaming with model-based red teaming in order to train Claude to be more harmless
Today we're introducing a new initiative to fund evaluations developed by third-party organizations that can effectively measure advanced capabilities in AI models
Priority Areas:
Claude Opus 4 achieves industry-leading results on SWE-bench for coding. It demonstrates strong performance on MMLU, GPQA, and Aider Polyglot
Sonnet 3.5 was successful at completing 90% of non-expert-level cyber tasks — outperforming its older version and GPT4o as a reference — and succeeded at 36% of cybersecurity apprentice-level tasks
At Anthropic, we spend a lot of time building evaluations to better understand our AI systems and share findings to advance the field.
Working with government agencies to establish evaluation standards for frontier AI models, contributing to policy development and safety protocols.
Publishing evaluation methodologies, constitutional frameworks, and safety research to benefit the broader AI safety community.
Start small, iterate, and scale: Start by writing just one to five questions or tasks, run a model on the evaluation, and read the model transcripts
Developing more sophisticated evaluation methods for increasingly capable AI systems, with focus on catastrophic risk prevention and alignment verification.
A robust, third-party evaluation ecosystem is essential for assessing AI capabilities and risks
Anthropic has established itself as a pioneer in AI evaluation testing through Constitutional AI, comprehensive safety assessments, and collaborative partnerships with government and academic institutions. Their approach combines rigorous internal evaluation with transparent external validation, setting industry standards for responsible AI development and deployment.
The company's emphasis on safety-first evaluation, from basic capability testing to advanced risk assessment, demonstrates how evaluation can be integrated throughout the AI development lifecycle to ensure reliable, beneficial, and safe AI systems.
Based on comprehensive research from Anthropic's published papers, policy documents, and collaborative evaluation initiatives