Revisiting the shutdown problem

Abstract:A key premise in leading arguments for existential risk from artificial intelligence is that malfunctioning artificial agents could not be easily shut down. This motivates the catastrophic shutdown problem of ensuring that agents can be shut down before they cause an existential catastrophe. A range of arguments and theorems are offered to suggest that solving the catastrophic shutdown problem is difficult, bolstering arguments for existential risk and motivating a search for solutions to the catastrophic shutdown problem. This paper argues for two conclusions. First, existing arguments do not establish the difficulty of solving the catastrophic shutdown problem. Second, concern for the catastrophic shutdown problem has led to technical solutions that impose a high safety tax on model performance.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2606.08296 [cs.AI]

(or arXiv:2606.08296v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.08296 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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