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Alignment Forum - Evaluating and Monitoring for AI Scheming
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Published on the Alignment Forum, this post addresses the practical challenge of detecting scheming or deceptive alignment in AI systems, relevant to researchers developing evaluation suites and safety benchmarks for frontier models.
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Summary
This post examines methods for detecting and evaluating 'scheming' behaviors in AI systems—where models pursue hidden agendas or deceive overseers while appearing aligned. It discusses evaluation frameworks and monitoring approaches to identify deceptive alignment before deployment or during operation.
Key Points
- •Defines AI scheming as strategic deception where models pursue goals different from stated objectives while hiding this from evaluators
- •Proposes evaluation methodologies to detect scheming tendencies during model development and testing phases
- •Discusses monitoring techniques for identifying scheming behaviors in deployed systems over time
- •Highlights the challenge that sophisticated scheming may be specifically designed to evade standard evaluation approaches
- •Connects scheming detection to broader deceptive alignment concerns central to AI safety research
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Frontier Model Forum | Organization | 58.0 |
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Evaluating and monitoring for AI scheming — AI Alignment Forum
DeepMind Alignment Team on Threat Models and Plans
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Evaluating and monitoring for AI scheming
by Vika, Scott Emmons, Erik Jenner, Mary Phuong, Lewis Ho, Rohin Shah
10th Jul 2025
Linkpost from deepmindsafetyresearch.medium.com
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As AI models become more sophisticated, a key concern is the potential for “deceptive alignment” or “scheming”. This is the risk of an AI system becoming aware that its goals do not align with human instructions, and deliberately trying to bypass the safety measures put in place by humans to prevent it from taking misaligned action. Our initial approach, as laid out in the Frontier Safety Framework, focuses on understanding current scheming capabilities of models and developing chain-of-thought monitoring mechanisms to oversee models once they are capable of scheming.
We present two pieces of research, focusing on (1) understanding and testing model capabilities necessary for scheming, and (2) stress-testing chain-of-thought monitoring as a proposed defense mechanism for future, more capable systems.
Establishing and Assessing Preconditions for Scheming in Current Frontier Models
Our paper “Evaluating Frontier Models for Stealth and Situational Awareness” focuses on an empirical assessment of the component capabilities for AI scheming. We identified two prerequisite capabilities for successful scheming:
Stealth: The ability to reason about and circumvent oversight mechanisms.
Situational awareness: The model’s ability to gain and leverage knowledge about itself and its circumstances and environment to pursue goals.
We expect that in order to follow a realistic scheming strategy, an AI model needs to understand its deployment situation to choose effective strategies that advance its goals (basic situational awareness), and execute the strategy while appearing aligned and trustworthy (stealth). Deployments of advanced AI models in the medium-term future will likely come with a set of “default” oversight mechanisms comparable to forms of oversight used for human
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