Paul Christiano 🇺🇸
AI safety researcher, founder and executive director of the Alignment Research Center, and senior technical advisor at NIST's Center for AI Standards and Innovation (CAISI). He serves on the OpenAI Foundation board and its Safety and Security Committee. He previously headed AI safety at the U.S. AI Safety Institute and CAISI, led OpenAI's language-model alignment team, and contributed to reinforcement learning from human feedback.
Alignment Research Center
- Evidence items
- 7
- Toward pressure
- 0.00
- Away pressure
- −0.26
- Net attributed pressure
- -0.26
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- institutional profileAlignment Research Center team
- institutional profileAnthropic Long-Term Benefit Trust
- personal sitePaul Christiano
- institutional profilePaul Christiano at NIST
- institutional profilePaul Christiano joins OpenAI Foundation Board
- personal sitePaul Christiano: current biography
- blogReturning to ARC (4 August 2026)
Assessments involving Paul Christiano
Paul Christiano joins OpenAI Foundation Board and safety committee
OpenAI appointed alignment researcher Paul Christiano to the OpenAI Foundation Board, its Safety and Security Committee, and a non-voting observer role on the OpenAI Group PBC board. The completed governance change adds an experienced external safety researcher to oversight of the nonprofit that controls OpenAI's public-benefit company.
Paul Christiano becomes head of AI safety at the U.S. AI Safety Institute
NIST appointed Christiano to lead frontier-model safety testing for capabilities of national-security concern and to help develop evaluation guidance and risk mitigations for the U.S. Artificial Intelligence Safety Institute.
ARC Evals becomes METR and begins separating into an independent nonprofit
ARC Evals adopted the METR name while wrapping up its incubation at the Alignment Research Center and transitioning toward a standalone nonprofit focused on measuring frontier-model autonomy and threat-relevant capabilities under Beth Barnes's leadership.
Paul Christiano describes independent pre-release evaluations for frontier labs
In a dated full interview, Christiano said the Alignment Research Center had conducted pre-release model evaluations for OpenAI and Anthropic, and argued that independent evaluators and external pressure are important for responsible lab policy.
Paul Christiano outlines two pathways by which advanced AI could erode human control
Paul Christiano argued that increasingly capable machine learning could cause a gradual loss of human influence by optimizing measurable proxies, or a sharper breakdown if influence-seeking policies learn to appear compliant, evade oversight, and exploit a period of systemic vulnerability.
Paul Christiano argues competition could force unsafe AI deployment before alignment
In a full 80,000 Hours interview, Paul Christiano identified competitive pressure as the main reason developers may be unable to slow down, arguing that actors could deploy systems effective at acquiring influence or prevailing in conflict before robust alignment is available. He paired the concern with debate and iterated-amplification approaches to scalable oversight.
Concrete Problems in AI Safety defines five practical accident-risk agendas
Chris Olah and collaborators framed negative side effects, reward hacking, scalable supervision, safe exploration and distribution shift as practical safety problems for advanced learning systems. OpenAI's companion release connected the agenda to concrete reinforcement-learning environments and evaluation work.