5 sources
A strong person to follow for how Anthropic moved from assistant training into more explicit evaluation work around model behavior, red-teaming, and chain-of-thought faithfulness.
Researcher Profile
Editor reviewedKamal Ndousse
Alignment via AI feedback (Constitutional AI)
Alignment and evaluation researcher at Anthropic
Worth following for the evaluation side of Anthropic’s alignment program, especially where model-written tests and public-input methods become practical tooling rather than just ideas.
Organizations
Anthropic
Labs
About This Page
This profile is meant to help you get oriented quickly: why this researcher matters, what to read first, and where to explore next.
Known For
The ideas, systems, and research directions that make this person worth knowing.
01
Model-written evaluations
02
Collective Constitutional AI
03
Assistant-alignment evaluation tooling
04
Alignment via AI feedback (Constitutional AI)
05
Constitutional AI: Harmlessness from AI Feedback
06
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
Start Here
Canonical papers, project pages, or repositories that anchor this profile.
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human FeedbackarticleDiscovering Language Model Behaviors with Model-Written EvaluationspaperCollective Constitutional AI: Aligning a Language Model with Public InputpaperConstitutional AI: Harmlessness from AI FeedbackpaperTraining a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackpaper
Supporting Sources
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Related Researchers
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5 sources
A good person to follow for the part of alignment work that becomes concrete measurement: model-written tests, chain-of-thought faithfulness, and behavior-shaping methods that can actually be audited.
5 sources
A good person to follow for the evaluation-heavy side of Anthropic alignment work, especially where early assistant training later feeds into reasoning-faithfulness and model-written testing.
4 sources
One of the earlier Anthropic contributors worth tracking if you care about the transition from RLHF-style assistant training into scaling and evaluation work.
5 sources
Useful for the seam between Anthropic’s earlier alignment papers and its later audit-oriented safety work, where interpretability and evaluation start feeding into deployment practice.
4 sources
Useful for the evaluation-heavy side of Anthropic’s research, especially where the lab moved from RLHF and Constitutional AI into broader behavior discovery.