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Anthropic's Work on AI Safety
paperCredibility Rating
4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: Anthropic
This is Anthropic's research landing page, useful as a starting point for discovering their published work on safety and alignment, but not a standalone paper or primary source in itself.
Metadata
Importance: 62/100homepage
Summary
Anthropic's research page aggregates their work across AI alignment, mechanistic interpretability, and societal impact assessment, all oriented toward understanding and mitigating risks from increasingly capable AI systems. It serves as a central hub for their published findings and ongoing safety-focused investigations.
Key Points
- •Covers multiple safety research domains including alignment, interpretability, and societal risk assessment.
- •Reflects Anthropic's mission to develop AI that is safe, beneficial, and understandable.
- •Includes empirical and theoretical research relevant to frontier AI model behavior.
- •Serves as an index for accessing specific papers and findings from Anthropic's research teams.
- •Research spans both technical safety work and broader questions about AI's societal implications.
Review
Anthropic's research strategy represents a comprehensive approach to AI safety, addressing critical challenges through specialized teams focusing on different aspects of AI development and deployment. Their work spans interpretability (understanding AI internal mechanisms), alignment (ensuring AI remains helpful and ethical), societal impacts (examining real-world AI interactions), and frontier risk assessment. The research approach is notable for its proactive and multifaceted methodology, combining technical research with policy considerations and empirical experiments. Key initiatives like Project Vend, constitutional classifiers, and introspection studies demonstrate their commitment to understanding AI behaviors, detecting potential misalignments, and developing robust safeguards. By investigating issues like alignment faking, jailbreak prevention, and AI's internal reasoning processes, Anthropic is pioneering approaches to create more transparent, controllable, and ethically-aligned artificial intelligence systems.
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# Research
Our research teams investigate the safety, inner workings, and societal impacts of AI models – so that artificial intelligence has a positive impact as it becomes increasingly capable.
Research teams: [Alignment](https://www.anthropic.com/research/team/alignment) [Economic Research](https://www.anthropic.com/research/team/economic-research) [Interpretability](https://www.anthropic.com/research/team/interpretability) [Societal Impacts](https://www.anthropic.com/research/team/societal-impacts)
### Interpretability
The mission of the Interpretability team is to discover and understand how large language models work internally, as a foundation for AI safety and positive outcomes.
### Alignment
The Alignment team works to understand the risks of AI models and develop ways to ensure that future ones remain helpful, honest, and harmless.
### Societal Impacts
Working closely with the Anthropic Policy and Safeguards teams, Societal Impacts is a technical research team that explores how AI is used in the real world.
### Frontier Red Team
The Frontier Red Team analyzes the implications of frontier AI models for cybersecurity, biosecurity, and autonomous systems.
[**Project Vend: Phase two** \\
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PolicyDec 18, 2025\\
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In June, we revealed that we’d set up a small shop in our San Francisco office lunchroom, run by an AI shopkeeper. It was part of Project Vend, a free-form experiment exploring how well AIs could do on complex, real-world tasks. How has Claude's business been since we last wrote?](https://www.anthropic.com/research/project-vend-2)
[InterpretabilityOct 29, 2025\\
**Signs of introspection in large language models** \\
Can Claude access and report on its own internal states? This research finds evidence for a limited but functional ability to introspect—a step toward understanding what's actually happening inside these models.](https://www.anthropic.com/research/introspection) [InterpretabilityMar 27, 2025\\
**Tracing the thoughts of a large language model** \\
Circuit tracing lets us watch Claude think, uncovering a shared conceptual space where reasoning happens before being translated into language—suggesting the model can learn something in one language and apply it in another.](https://www.anthropic.com/research/tracing-thoughts-language-model) [AlignmentFeb 3, 2025\\
**Constitutional Classifiers: Defending against universal jailbreaks** \\
These classifiers filter the overwhelming majority of jailbreaks while maintaining practical deployment. A prototype withstood over 3,000 hours of red teaming with no universal jailbreak discovered.](https://www.anthropic.com/research/constitutional-classifiers) [AlignmentDec 18, 2024\\
**Alignment faking in large language models** \\
This paper provides the first empirical example of a model en
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