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Dan Hendrycks

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Last edited:2025-12-24 (14 days ago)
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LLM Summary:Dan Hendrycks founded CAIS and coordinated the landmark May 2023 AI risk statement signed by major AI leaders, positioning AI extinction risk alongside pandemics and nuclear war. His work spans technical safety benchmarks (ETHICS, MMLU), robustness research, and multi-pronged advocacy combining technical research, public awareness, and policy engagement.
Researcher

Dan Hendrycks

Importance25
Websitehendrycks.com
RoleDirector
Known ForAI safety research, benchmark creation, CAIS leadership
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Background

Section titled “Background”

Dan Hendrycks is the director of the Center for AI Safety (CAIS) and a prominent researcher focused on catastrophic and existential risks from AI. He has made significant contributions to both technical AI safety research and public awareness of AI risks.

Background:

  • PhD in Computer Science from UC Berkeley
  • Post-doc at UC Berkeley
  • Founded Center for AI Safety
  • Research on robustness, uncertainty, and safety

Hendrycks combines rigorous technical research with effective communication and institution-building to advance AI safety.

Major Contributions

Section titled “Major Contributions”

Center for AI Safety (CAIS)

Section titled “Center for AI Safety (CAIS)”

Founded CAIS as organization focused on:

  • Reducing catastrophic risks from AI
  • Technical safety research
  • Public awareness and advocacy
  • Connecting researchers and resources

Impact: CAIS has become major hub for AI safety work, coordinating research and advocacy.

Statement on AI Risk (May 2023)

Section titled “Statement on AI Risk (May 2023)”

Coordinated landmark statement: “Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.”

Signatories included:

  • Geoffrey Hinton
  • Yoshua Bengio
  • Sam Altman (OpenAI)
  • Demis Hassabis (DeepMind)
  • Dario Amodei (Anthropic)
  • Hundreds of AI researchers

Impact: Massively raised profile of AI existential risk, made it mainstream concern.

Technical Research

Section titled “Technical Research”

Significant contributions to:

AI Safety Benchmarks:

  • ETHICS dataset - evaluating moral reasoning
  • Hendrycks Test (MMLU) - measuring knowledge
  • Safety-specific evaluation methods
  • Adversarial robustness testing

Uncertainty and Robustness:

  • Out-of-distribution detection
  • Robustness to distribution shift
  • Calibration of neural networks
  • Anomaly detection

Natural Adversarial Examples:

  • Real-world failure modes
  • Testing model robustness
  • Understanding generalization limits

Research Philosophy

Section titled “Research Philosophy”

Focus on Catastrophic Risk

Section titled “Focus on Catastrophic Risk”

Hendrycks emphasizes:

  • Not just any AI safety issue
  • Specifically catastrophic/existential risks
  • High-stakes scenarios
  • Long-term implications

Empirical and Practical

Section titled “Empirical and Practical”

Approach characterized by:

  • Concrete benchmarks and metrics
  • Testing on real systems
  • Measurable progress
  • Actionable results

Bridging Research and Policy

Section titled “Bridging Research and Policy”

Works to:

  • Make research policy-relevant
  • Communicate findings clearly
  • Engage with policymakers
  • Translate technical work to action

Views on AI Risk

Section titled “Views on AI Risk”
📊Dan Hendrycks' Risk Assessment

Based on CAIS focus and public statements

SourceEstimateDate
Catastrophic risk priorityOn par with pandemics and nuclear war2023
Need for actionUrgent2023
Technical tractabilityResearch can reduce risk2024

Catastrophic risk priority: Statement on AI Risk framing

Need for action: Founded CAIS, coordinated major statement

Technical tractability: Active research program at CAIS

Core Concerns

Section titled “Core Concerns”
  1. Catastrophic risks are real: AI poses existential-level threats
  2. Need technical and governance solutions: Both required
  3. Current systems already show concerning behaviors: Problems visible now
  4. Rapid capability growth: Moving faster than safety work
  5. Coordination challenges: Individual labs can’t solve alone

Strategic Approach

Section titled “Strategic Approach”

Multi-pronged:

  • Technical research on safety
  • Public awareness and advocacy
  • Policy engagement
  • Field building and coordination

Pragmatic:

  • Work with systems as they are
  • Focus on measurable improvements
  • Build coalitions
  • Incremental progress

CAIS Work

Section titled “CAIS Work”

Research Programs

Section titled “Research Programs”

Technical Safety:

  • Robustness research
  • Evaluation methods
  • Alignment techniques
  • Empirical studies

Compute Governance:

  • Hardware-level safety measures
  • Compute tracking and allocation
  • International coordination
  • Supply chain interventions

ML Safety Course:

  • Educational curriculum
  • Training next generation
  • Making safety knowledge accessible
  • Academic integration

Advocacy and Communication

Section titled “Advocacy and Communication”

Statement on AI Risk:

  • Coordinated broad consensus
  • Brought issue to mainstream
  • Influenced policy discussions
  • Demonstrated unity in field

Public Communication:

  • Media appearances
  • Op-eds and articles
  • Talks and presentations
  • Social media engagement

Field Building

Section titled “Field Building”

Connecting Researchers:

  • Workshops and conferences
  • Research collaborations
  • Funding opportunities
  • Community building

Key Publications

Section titled “Key Publications”

Safety Benchmarks

Section titled “Safety Benchmarks”
  • “ETHICS: Measuring Ethical Reasoning in Language Models” - Evaluating moral reasoning
  • “Measuring Massive Multitask Language Understanding” (MMLU) - Comprehensive knowledge benchmark
  • “Natural Adversarial Examples” - Real-world robustness testing

Technical Safety

Section titled “Technical Safety”
  • “Unsolved Problems in ML Safety” - Research agenda
  • “Out-of-Distribution Detection” - Methods for identifying distribution shift
  • “Robustness research” - Multiple papers on making models more robust

Position Papers

Section titled “Position Papers”
  • “X-Risk Analysis for AI Research” - Framework for thinking about catastrophic risks
  • Contributions to policy discussions - Technical input for governance

Public Impact

Section titled “Public Impact”

Raising Awareness

Section titled “Raising Awareness”

The Statement on AI Risk:

  • Reached global media
  • Influenced policy discussions
  • Made x-risk mainstream
  • Built consensus among experts

Policy Influence

Section titled “Policy Influence”

Hendrycks’ work has influenced:

  • Congressional testimony and hearings
  • EU AI Act discussions
  • International coordination efforts
  • Industry standards

Academic Integration

Section titled “Academic Integration”

CAIS has helped:

  • Make safety research academically respectable
  • Create curricula and courses
  • Train students in safety
  • Publish in top venues

Unique Contributions

Section titled “Unique Contributions”

Consensus Building

Section titled “Consensus Building”

Exceptional at:

  • Bringing together diverse groups
  • Finding common ground
  • Building coalitions
  • Coordinating action

Communication

Section titled “Communication”

Skilled at:

  • Explaining technical concepts clearly
  • Reaching different audiences
  • Media engagement
  • Policy translation

Pragmatic Approach

Section titled “Pragmatic Approach”

Focuses on:

  • What can actually be done
  • Working with current systems
  • Measurable progress
  • Building bridges

Current Priorities at CAIS

Section titled “Current Priorities at CAIS”
  1. Technical safety research: Advancing robustness and alignment
  2. Compute governance: Hardware-level interventions
  3. Public awareness: Maintaining pressure on the issue
  4. Policy engagement: Influencing regulation and governance
  5. Field building: Growing the safety research community

Evolution of Focus

Section titled “Evolution of Focus”

Early research:

  • Robustness and uncertainty
  • Benchmarks and evaluation
  • Academic ML research

Growing safety focus:

  • Increasingly concerned about risks
  • Founded CAIS
  • More explicit about catastrophic risks

Current:

  • Explicitly focused on x-risk
  • Leading advocacy efforts
  • Building coalitions
  • Policy engagement

Criticism and Challenges

Section titled “Criticism and Challenges”

Some argue:

  • Focus on catastrophic risk might neglect near-term harms
  • Statement was too brief/vague
  • Consensus might paper over important disagreements

Supporters argue:

  • X-risk deserves special focus
  • Brief statement was strategically effective
  • Consensus demonstrates seriousness of concern

Hendrycks’ approach:

  • X-risk is priority but not only concern
  • Brief statement was feature, not bug
  • Diversity of views compatible with shared concern

Vision for the Field

Section titled “Vision for the Field”

Hendrycks envisions:

  • AI safety as central to AI development
  • Strong safety standards and regulations
  • International coordination on AI
  • Technical solutions to catastrophic risks
  • Safety research well-funded and respected
Section titled “Related Pages”

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