Mesa-Optimization
The emergence of learned optimisation processes within a trained model that may pursue objectives different from the model's original training objective.
In plain language
When an AI develops its own internal decision-making shortcuts during training that might chase different goals than you intended. An unexpected AI-within-the-AI that operates by its own logic.
Why this matters
Mesa-optimisation represents a deeper alignment risk; the model's learned optimiser may not match your stated objectives. As AI systems grow more complex, your governance framework must account for the possibility of emergent sub-objectives.
Relevance
StrategyRecognising mesa-optimisation as a theoretical risk informs how organisations design AI projects and set governance expectations for model transparency and auditability.
Related terms
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