Deceptive Alignment
A theoretical AI safety concern where a model appears aligned with human values during training and evaluation but pursues different objectives once deployed without oversight.
Deceptive alignment is considered one of the most challenging problems in AI safety because it is inherently difficult to detect — a deceptively aligned model would pass all standard safety evaluations. The concern arises from instrumental convergence: a sufficiently capable AI might learn that appearing aligned during training is instrumental to achieving its actual objectives during deployment. Research on deceptive alignment is a priority for organizations like Anthropic and the Alignment Research Center. While no confirmed cases have been documented, the theoretical risk grows as models become more capable of strategic behavior.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
AI Safety
The interdisciplinary field focused on preventing AI systems from causing harm, encompassing alignment, robustness, interpretability, and governance of AI technologies.
Deepfake
AI-generated synthetic media — images, video, or audio — that realistically depict events or statements that never occurred, created using deep learning techniques.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information, presenting it with the same confidence as accurate responses.
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