Shutdown-seeking AI

The paper presents a unique approach to AI safety by suggesting the development of artificial intelligence systems with a singular goal of shutdown. Unlike traditional alignment strategies that attempt to create goals matching human values, this 'beneficial goal misalignment' approach proposes an AI that fundamentally wants to be turned off. The authors argue this strategy offers three key benefits: improved specification in reinforcement learning, reduced risks from instrumental convergence, and a built-in 'tripwire' for monitoring dangerous capabilities. The methodology involves carefully designing an AI's environment so that shutdown is only possible after completing beneficial tasks, creating a safety mechanism that prevents uncontrolled AI behavior. While acknowledging potential challenges like manipulation risks, the authors contend that shutdown-seeking AIs could provide a pragmatic approach to AI safety by ensuring that any developed dangerous capabilities would result in self-termination. The proposal represents an innovative perspective in AI safety research, offering a provocative alternative to existing alignment frameworks by fundamentally reimagining the goal structure of artificial intelligence.