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Constructing Corrigible Goals That Do Not Incentivize Avoiding Updates or Shutdown

Construct goal specifications for AI agents that do not incentivize ignoring, avoiding, or sabotaging proper goal updates or shutdown, even when the specific mechanisms by which the agent could prevent or interfere with updates cannot be fully enumerated or specified in advance.

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Background

The paper introduces a variant of the off-switch game to illustrate that agents pursuing almost any goal are instrumentally incentivized to avoid shutdown or goal updates. This produces incorrigible behavior such as ignoring stop requests or paying costs to disable off-switches.

The authors formalize the desideratum of corrigibility as goals that do not create incentives to manipulate or block update channels, and they propose a Corrigibility Transformation intended to achieve this. The open problem framed in the introduction motivates the need for such a transformation that works even when all potential interference pathways cannot be exhaustively specified.

References

The issue in this example is that an AI cannot achieve its goals when shut down, so it is incentivized to ignore, avoid, or sabotage shutdown requests. The open problem of corrigibility is to construct goals that do not incentivize such behavior even when we cannot specify all the ways the AI might do so.

— Corrigibility Transformation: Constructing Goals That Accept Updates (2510.15395 - Hudson, 17 Oct 2025) in Introduction