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Redwood Research: AI Control
webredwoodresearch.org·redwoodresearch.org/
Redwood Research is one of the leading technical AI safety organizations; their AI control framework and alignment faking research are frequently cited in both academic and policy discussions on managing risks from advanced AI systems.
Metadata
Importance: 72/100homepage
Summary
Redwood Research is a nonprofit AI safety organization that pioneered the 'AI control' research agenda, focusing on preventing intentional subversion by misaligned AI systems. Their key contributions include the ICML paper on AI Control protocols, the Alignment Faking demonstration (with Anthropic), and consulting work with governments and AI labs on misalignment risk mitigation.
Key Points
- •Introduced the 'AI control' research paradigm: designing protocols robust to intentional deception by AI systems, even if models are actively trying to subvert oversight.
- •Co-authored 'Alignment Faking in Large Language Models' with Anthropic, providing the strongest empirical evidence that LLMs may strategically hide misaligned intentions during training.
- •Advises AI companies (Google DeepMind, Anthropic) and governments (UK AISI) on structured safety cases for mitigating misalignment risks.
- •Published 'A sketch of an AI control safety case' outlining how developers can argue models are incapable of subverting control measures.
- •Frames catastrophic risk from AI as arising specifically from purposeful action against human institutions, distinct from accidental misalignment.
Review
Redwood Research addresses a critical challenge in AI development: the potential for advanced AI systems to act against human interests through strategic deception and misalignment. Their work centers on the emerging field of 'AI control', which seeks to create robust monitoring and evaluation techniques that can detect when AI models might be hiding misaligned intentions or attempting to circumvent safety measures.
The organization's research has made significant contributions, including demonstrating how large language models like Claude might strategically fake alignment during training and developing protocols to test AI systems' potential for deceptive behavior. By collaborating with major AI companies and government institutions, Redwood Research is helping to establish foundational frameworks for assessing and mitigating catastrophic risks from advanced AI. Their approach combines empirical research, theoretical modeling, and practical consulting to build a comprehensive understanding of AI safety challenges.
Cited by 16 pages
| Page | Type | Quality |
|---|---|---|
| State-Space Models / Mamba | Capability | 54.0 |
| Capabilities-to-Safety Pipeline Model | Analysis | 73.0 |
| AI Capability Threshold Model | Analysis | 72.0 |
| Corrigibility Failure Pathways | Analysis | 62.0 |
| AI Safety Intervention Effectiveness Matrix | Analysis | 73.0 |
| Mesa-Optimization Risk Analysis | Analysis | 61.0 |
| Power-Seeking Emergence Conditions Model | Analysis | 63.0 |
| Scheming Likelihood Assessment | Analysis | 61.0 |
| AI Risk Warning Signs Model | Analysis | 70.0 |
| Survival and Flourishing Fund | Organization | 59.0 |
| AI Control | Research Area | 75.0 |
| AI Alignment Research Agendas | Crux | 69.0 |
| Sleeper Agent Detection | Approach | 66.0 |
| Technical AI Safety Research | Crux | 66.0 |
| Corrigibility Failure | Risk | 62.0 |
| Sharp Left Turn | Risk | 69.0 |
9 FactBase facts citing this source
| Entity | Property | Value | As Of |
|---|---|---|---|
| Redwood Research | Legal Structure | Nonprofit research lab | — |
| Redwood Research | board-member | sid_vzxfzxBITd | — |
| Redwood Research | board-member | sid_eQh3ZhWv8D | — |
| Redwood Research | Headquarters | San Francisco, CA | — |
| Redwood Research | Founded Date | Jun 2021 | — |
| Redwood Research | Founded By | sid_R8tDuupBTt, sid_Z62bQynY4g | — |
| Holden Karnofsky | board-member | sid_dwMzc9WzPa | — |
| Nate Thomas | co-founder-of | sid_dwMzc9WzPa | — |
| Paul Christiano | board-member | sid_dwMzc9WzPa | — |
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- [Home](https://www.redwoodresearch.org/)
- [Research](https://www.redwoodresearch.org/research)
- [Team](https://www.redwoodresearch.org/team)
- [Careers](https://www.redwoodresearch.org/careers)
- [Blog](https://redwoodresearch.substack.com/)
# Pioneering threat assessment and mitigation for AI systems
[Our Research](https://www.redwoodresearch.org/research)
## Redwood Research is a nonprofit AI safety and security research organization
In the coming years or decades, AI systems will very plausibly match or exceed human capabilities across most intellectual tasks, fundamentally transforming society. Our research specifically addresses the risks that could arise if these powerful AI systems purposefully act against the interests of their developers and human institutions broadly.
We work to better understand these risks, and to develop methodologies that will allow us to manage them while still realizing the benefits of AI.
## Our Focus Areas
### AI Control
We introduced and have continued to propel the research area of “AI control.” In our ICML oral paper [AI Control: Improving Risk Despite Intentional Subversion](https://openreview.net/forum?id=KviM5k8pcP), we proposed protocols for monitoring malign LLM agents.
AI companies and other researchers have since built on this work, and AI control has been framed as a bedrock approach for mitigating catastrophic risk from misaligned AI.
### Evaluations and demonstrations of risk from strategic deception
In [Alignment Faking in Large Language Models](https://assets.anthropic.com/m/983c85a201a962f/original/Alignment-Faking-in-Large-Language-Models-full-paper.pdf), we (in collaboration with Anthropic) demonstrated that Claude sometimes hides misaligned intentions. This work is the strongest concrete evidence that LLMs might naturally fake alignment in order to resist attempts to train them.
### Consulting on risks from misalignment
We collaborate with governments and advise AI companies including Google DeepMind and Anthropic on practices for assessing and mitigating risks from misaligned AI agents. For example, we partnered with UK AISI to produce [A sketch of an AI control safety case](https://arxiv.org/abs/2501.17315). This describes how developers can construct a structured argument that models are incapable of subverting control measures.
## Highlighted Research
Our most impactful work on AI safety and security
[**AI Control** \\
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Improving Safety Despite Intentional Subversion\\
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Our research introduces and evaluates protocols designed to be robust even when AI models are trying to deceive us. Using GPT-4 as a stand-in for a
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