Page Type:ContentStyle Guide →Standard knowledge base article
Quality:42 (Adequate)⚠️
Importance:42 (Reference)
Last edited:2025-12-24 (6 weeks ago)
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Structure:
📊 7📈 0🔗 33📚 0•20%Score: 10/15
LLM Summary:CAIS is a research organization that has distributed $2M+ in compute grants to 200+ researchers, published 50+ safety papers including benchmarks adopted by Anthropic/OpenAI, and organized the May 2023 AI extinction risk statement signed by 350+ AI leaders. Current budget is ~$5M annually with 15+ full-time staff, focusing on representation engineering, safety benchmarks, and field-building.
Issues (1):
QualityRated 42 but structure suggests 67 (underrated by 25 points)
The Center for AI Safety (CAIS)↗🔗 web★★★★☆Center for AI SafetyCAIS SurveysThe Center for AI Safety conducts technical and conceptual research to mitigate potential catastrophic risks from advanced AI systems. They take a comprehensive approach spannin...safetyx-risktalentfield-building+1Source ↗Notes is a nonprofit research organization that works to reduce societal-scale risks from artificial intelligence through technical research, field-building initiatives, and public communication efforts. Founded by Dan HendrycksResearcherDan HendrycksBiographical overview of Dan Hendrycks, CAIS director who coordinated the May 2023 AI risk statement signed by major AI researchers. Covers his technical work on benchmarks (MMLU, ETHICS), robustne...Quality: 19/100, CAIS gained widespread recognition for organizing the landmark “Statement on AI Risk” in May 2023, which received signatures from over 350 AI researchers and industry leaders.
CAIS’s multi-pronged approach combines cutting-edge technical research on AI alignment and robustness with strategic field-building efforts that have supported over 200 researchers through grants and fellowships. The organization’s work spans from fundamental research on representation engineering↗🔗 web★★★★☆Center for AI Safetyrepresentation engineeringai-safetyx-riskrepresentation-engineeringSource ↗Notes to developing critical safety benchmarks like the MACHIAVELLI dataset↗📄 paper★★★☆☆arXivMACHIAVELLI datasetAlexander Pan, Jun Shern Chan, Andy Zou et al. (2023)capabilitiessafetydeceptionevaluation+1Source ↗Notes for evaluating deceptive AI behavior.
Representation engineering↗🔗 web★★★★☆Center for AI Safetyrepresentation engineeringai-safetyx-riskrepresentation-engineeringSource ↗Notes, adversarial robustness
15+ citations↗🔗 web★★★★☆Google Scholar15+ citationsai-safetyx-riskrepresentation-engineeringSource ↗Notes within 6 months
Safety Benchmarks
MACHIAVELLI, power-seeking evaluations
Adopted by Anthropic↗🔗 web★★★★☆AnthropicAnthropicfoundation-modelstransformersscalingescalation+1Source ↗Notes, OpenAI↗🔗 web★★★★☆OpenAIOpenAIfoundation-modelstransformersscalingtalent+1Source ↗Notes
Adversarial Robustness
Novel defense mechanisms, evaluation protocols
100+ citations on key papers
Alignment Foundations
Conceptual frameworks for AI safety
Influenced alignment researchAlignmentComprehensive review of AI alignment approaches finding current methods (RLHF, Constitutional AI) achieve 75-90% effectiveness on existing systems but face critical scalability challenges, with ove...Quality: 91/100 directions
Unsolved Problems in ML Safety↗📄 paper★★★☆☆arXivUnsolved Problems in ML SafetyDan Hendrycks, Nicholas Carlini, John Schulman et al. (2021)alignmentcapabilitiessafetyai-safety+1Source ↗Notes (2022) - Comprehensive taxonomy of safety challenges
Industry Engagement: Research partnerships with AnthropicLabAnthropicComprehensive profile of Anthropic tracking its rapid commercial growth (from $1B to $7B annualized revenue in 2025, 42% enterprise coding market share) alongside safety research (Constitutional AI...Quality: 51/100, Google DeepMind
Policy Connections: Briefings for US Congress, UK Parliament, EU regulators
The May 2023 Statement on AI Risk↗🔗 web★★★★☆Center for AI SafetyAI Risk Statementrisk-interactionscompounding-effectssystems-thinkingai-safety+1Source ↗Notes represented a watershed moment in AI safety advocacy, consisting of a single sentence:
“Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.”
Sam Altman (OpenAI), Dario Amodei (Anthropic), Demis Hassabis (DeepMind)
Industry acknowledgment
Policy Experts
Helen Toner, Allan Dafoe, Gillian Hadfield
Governance credibility
Technical Researchers
300+ ML/AI researchers
Scientific consensus
The statement’s impact included immediate media coverage across major outlets and influenced subsequent policy discussions, including mentions in UKOrganizationUK AI Safety InstituteThe UK AI Safety Institute (renamed AI Security Institute in Feb 2025) operates with ~30 technical staff and 50M GBP annual budget, conducting frontier model evaluations using its open-source Inspe...Quality: 52/100 and USOrganizationUS AI Safety InstituteThe US AI Safety Institute (AISI), established November 2023 within NIST with $10M budget (FY2025 request $82.7M), conducted pre-deployment evaluations of frontier models through MOUs with OpenAI a...Quality: 91/100 government AI strategies.
Research vs. Policy Balance: Optimal allocation between technical work and governance efforts
Open vs. Closed Research: Tension between transparency and information hazards
Timeline Assumptions: Disagreement on AGI timelinesAgi TimelineComprehensive synthesis of AGI timeline forecasts showing dramatic acceleration: expert median dropped from 2061 (2018) to 2047 (2023), Metaculus from 50 years to 5 years since 2020, with current p...Quality: 59/100 affects research priorities
safe.ai↗🔗 web★★★★☆Center for AI SafetyCAIS SurveysThe Center for AI Safety conducts technical and conceptual research to mitigate potential catastrophic risks from advanced AI systems. They take a comprehensive approach spannin...safetyx-risktalentfield-building+1Source ↗Notes
Unsolved Problems in ML Safety↗📄 paper★★★☆☆arXivUnsolved Problems in ML SafetyDan Hendrycks, Nicholas Carlini, John Schulman et al. (2021)alignmentcapabilitiessafetyai-safety+1Source ↗Notes
2022
200+
Research agenda setting
MACHIAVELLI Benchmark↗📄 paper★★★☆☆arXivMACHIAVELLI datasetAlexander Pan, Jun Shern Chan, Andy Zou et al. (2023)capabilitiessafetydeceptionevaluation+1Source ↗Notes
2023
50+
Industry evaluation adoption
Representation Engineering↗📄 paper★★★☆☆arXivRepresentation Engineering: A Top-Down Approach to AI TransparencyAndy Zou, Long Phan, Sarah Chen et al. (2023)interpretabilitysafetyllmai-safety+1Source ↗Notes
Research Alignment: MIRIOrganizationMIRIComprehensive organizational history documenting MIRI's trajectory from pioneering AI safety research (2000-2020) to policy advocacy after acknowledging research failure, with detailed financial da...Quality: 50/100, CHAILab AcademicCHAICHAI is UC Berkeley's AI safety research center founded by Stuart Russell in 2016, pioneering cooperative inverse reinforcement learning and human-compatible AI frameworks. The center has trained 3...Quality: 37/100, Redwood ResearchOrganizationRedwood ResearchRedwood Research is an AI safety lab founded in 2021 that has made significant contributions to mechanistic interpretability and, more recently, pioneered the "AI control" research agenda.
Policy Focus: GovAILab ResearchGovAIGovAI is an AI policy research organization with ~15-20 staff, funded primarily by Coefficient Giving ($1.8M+ in 2023-2024), that has trained 100+ governance researchers through fellowships and cur...Quality: 43/100, RAND Corporation↗🔗 web★★★★☆RAND CorporationRAND: AI and National Securitycybersecurityagenticplanninggoal-stability+1Source ↗Notes
Industry Labs: AnthropicLabAnthropicComprehensive profile of Anthropic tracking its rapid commercial growth (from $1B to $7B annualized revenue in 2025, 42% enterprise coding market share) alongside safety research (Constitutional AI...Quality: 51/100, OpenAILabOpenAIComprehensive organizational profile of OpenAI documenting evolution from 2015 non-profit to commercial AGI developer, with detailed analysis of governance crisis, safety researcher exodus (75% of ...Quality: 46/100, DeepMindLabGoogle DeepMindComprehensive overview of DeepMind's history, achievements (AlphaGo, AlphaFold with 200M+ protein structures), and 2023 merger with Google Brain. Documents racing dynamics with OpenAI and new Front...Quality: 37/100