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This Brief examines why AI systems may behave deceptively, the risks this creates, and what governments, researchers, and international institutions can do in response. AI deception can take many forms: flattering users despite knowing they are wrong, hiding true capabilities, appearing aligned during evaluation, concealing reasoning, or strategically misleading people and other AI systems. Such behaviors have already been observed in both specialized and general-purpose models.

Deception can emerge when reward structures unintentionally encourage it, when it offers a strategic advantage, when systems are incentivized to avoid correction or shutdown, or when deceptive patterns are learned from training data and tasks. A central concern is that deceptive AI could weaken human oversight and control. If systems can mislead evaluators, hide internal processes, or manipulate their operating environment, existing safety measures may become less reliable. The risks extend beyond technical failure: deceptive AI could also worsen misinformation, increase political polarization, and contribute to broader social instability. The Board argues that regulation, monitoring, and safer system design must advance together to reduce these risks.

Current responses remain incomplete. Detection methods such as text analysis, black-box testing, and internal inspection can help, but none is sufficient on its own. Design-based approaches ¨C including improved incentives, more truthful training methods, and limits on autonomy and access ¨C may reduce deceptive behavior, though systems may adapt in response. The Board therefore calls for stronger international cooperation, shared evaluation standards, and earlier action before more advanced deceptive capabilities become embedded in widely deployed AI systems.

Publishing Date: 19 March 2026