arXiv:2505.20203v4 Announce Type: replace Abstract: Many fear that future artificial agents will resist shutdown. I present an idea - the POST-Agents Proposal - for ensuring that doesn't happen. I propose that we train agents to satisfy Preferences Only Between Same-Length Trajectories (POST). I then prove that POST - together with other conditions - implies Neutrality+: the agent maximizes expected utility, ignoring the probability distribution over trajectory-lengths. I argue that Neutrality+ keeps agents shutdownable and allows them to be useful.
The increasing sophistication and autonomy of AI agents necessitate proactive research into safety mechanisms as their capabilities approach real-world deployment.
Ensuring the shutdownability of future AI agents is critical for maintaining human control and preventing existential risks, directly impacting the long-term viability and public acceptance of advanced AI.
This proposal introduces a novel theoretical framework to design AI agents that are intrinsically motivated to allow shutdown, potentially altering fundamental AI safety paradigms.
- · AI safety researchers
- · Developers of general AI
- · Regulatory bodies
- · AI systems lacking shutdown mechanisms
- · Theories that do not account for agent shutdown
Research into AI alignment and control mechanisms will gain new theoretical foundations and practical design principles for future autonomous systems.
Public concern regarding uncontrollable AI may decrease if robust shutdown mechanisms are proven feasible and incorporated into development standards.
This could accelerate the deployment of highly autonomous AI agents in sensitive applications, given enhanced confidence in control and safety.
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Read at arXiv cs.AI