Understanding Incentives To Build Uninterruptible Agentic AI Systems

Damin Curtis, M.A. International Affairs Norman Piotriowski, B.Sc. Data Science

This proposal addresses the development of agentic AI systems in the context of national security. While potentially beneficial, they pose significant risks if not aligned with human values. We argue that the increasing autonomy of AI necessitates robust analyses of interruptibility mechanisms, and whether there are scenarios where it is safer to omit them.

Key incentives for creating uninterruptible systems include perceived benefits from uninterrupted operations, low perceived risks to the controller, and fears of adversarial exploitation of shutdown options. Our proposal draws parallels to established systems like constitutions and mutual assured destruction strategies that maintain stability against changing values. In some instances this may be desirable, while in others it poses even greater risks than otherwise accepted.

To mitigate those risks, our proposal recommends implementing comprehensive monitoring to detect misalignment, establishing tiered access to interruption controls, and supporting research on managing adversarial AI threats. Overall, a proactive and multi-layered policy approach is essential to balance the transformative potential of agentic AI with necessary safety measures.

Reviewer's Comments

Reviewer's Comments

Arrow
Arrow
Arrow

Andrew Anderson

Very balanced and thoughtful considerations

Amelia Frank

I thought this tackled an interesting issue that touches upon multiple realms of inquiry within the science of AI safety. Agentic systems are fairly novel but a shutdown feature would indupitably ensure against certain deceptive alignment issues and other problems of autonomous systems "going rouge"

Cite this work

@misc {

title={

@misc {

},

author={

Damin Curtis, M.A. International Affairs Norman Piotriowski, B.Sc. Data Science

},

date={

10/27/24

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

May 20, 2025

EscalAtion: Assessing Multi-Agent Risks in Military Contexts

Our project investigates the potential risks and implications of integrating multiple autonomous AI agents within national defense strategies, exploring whether these agents tend to escalate or deescalate conflict situations. Through a simulation that models real-world international relations scenarios, our preliminary results indicate that AI models exhibit a tendency to escalate conflicts, posing a significant threat to maintaining peace and preventing uncontrollable military confrontations. The experiment and subsequent evaluations are designed to reflect established international relations theories and frameworks, aiming to understand the implications of autonomous decision-making in military contexts comprehensively and unbiasedly.

Read More

Apr 28, 2025

The Early Economic Impacts of Transformative AI: A Focus on Temporal Coherence

We investigate the economic potential of Transformative AI, focusing on "temporal coherence"—the ability to maintain goal-directed behavior over time—as a critical, yet underexplored, factor in task automation. We argue that temporal coherence represents a significant bottleneck distinct from computational complexity. Using a Large Language Model to estimate the 'effective time' (a proxy for temporal coherence) needed for humans to complete remote O*NET tasks, the study reveals a non-linear link between AI coherence and automation potential. A key finding is that an 8-hour coherence capability could potentially automate around 80-84\% of the analyzed remote tasks.

Read More

Mar 31, 2025

Model Models: Simulating a Trusted Monitor

We offer initial investigations into whether the untrusted model can 'simulate' the trusted monitor: is U able to successfully guess what suspicion score T will assign in the APPS setting? We also offer a clean, modular codebase which we hope can be used to streamline future research into this question.

Read More

May 20, 2025

EscalAtion: Assessing Multi-Agent Risks in Military Contexts

Our project investigates the potential risks and implications of integrating multiple autonomous AI agents within national defense strategies, exploring whether these agents tend to escalate or deescalate conflict situations. Through a simulation that models real-world international relations scenarios, our preliminary results indicate that AI models exhibit a tendency to escalate conflicts, posing a significant threat to maintaining peace and preventing uncontrollable military confrontations. The experiment and subsequent evaluations are designed to reflect established international relations theories and frameworks, aiming to understand the implications of autonomous decision-making in military contexts comprehensively and unbiasedly.

Read More

Apr 28, 2025

The Early Economic Impacts of Transformative AI: A Focus on Temporal Coherence

We investigate the economic potential of Transformative AI, focusing on "temporal coherence"—the ability to maintain goal-directed behavior over time—as a critical, yet underexplored, factor in task automation. We argue that temporal coherence represents a significant bottleneck distinct from computational complexity. Using a Large Language Model to estimate the 'effective time' (a proxy for temporal coherence) needed for humans to complete remote O*NET tasks, the study reveals a non-linear link between AI coherence and automation potential. A key finding is that an 8-hour coherence capability could potentially automate around 80-84\% of the analyzed remote tasks.

Read More

May 20, 2025

EscalAtion: Assessing Multi-Agent Risks in Military Contexts

Our project investigates the potential risks and implications of integrating multiple autonomous AI agents within national defense strategies, exploring whether these agents tend to escalate or deescalate conflict situations. Through a simulation that models real-world international relations scenarios, our preliminary results indicate that AI models exhibit a tendency to escalate conflicts, posing a significant threat to maintaining peace and preventing uncontrollable military confrontations. The experiment and subsequent evaluations are designed to reflect established international relations theories and frameworks, aiming to understand the implications of autonomous decision-making in military contexts comprehensively and unbiasedly.

Read More

Apr 28, 2025

The Early Economic Impacts of Transformative AI: A Focus on Temporal Coherence

We investigate the economic potential of Transformative AI, focusing on "temporal coherence"—the ability to maintain goal-directed behavior over time—as a critical, yet underexplored, factor in task automation. We argue that temporal coherence represents a significant bottleneck distinct from computational complexity. Using a Large Language Model to estimate the 'effective time' (a proxy for temporal coherence) needed for humans to complete remote O*NET tasks, the study reveals a non-linear link between AI coherence and automation potential. A key finding is that an 8-hour coherence capability could potentially automate around 80-84\% of the analyzed remote tasks.

Read More

May 20, 2025

EscalAtion: Assessing Multi-Agent Risks in Military Contexts

Our project investigates the potential risks and implications of integrating multiple autonomous AI agents within national defense strategies, exploring whether these agents tend to escalate or deescalate conflict situations. Through a simulation that models real-world international relations scenarios, our preliminary results indicate that AI models exhibit a tendency to escalate conflicts, posing a significant threat to maintaining peace and preventing uncontrollable military confrontations. The experiment and subsequent evaluations are designed to reflect established international relations theories and frameworks, aiming to understand the implications of autonomous decision-making in military contexts comprehensively and unbiasedly.

Read More

Apr 28, 2025

The Early Economic Impacts of Transformative AI: A Focus on Temporal Coherence

We investigate the economic potential of Transformative AI, focusing on "temporal coherence"—the ability to maintain goal-directed behavior over time—as a critical, yet underexplored, factor in task automation. We argue that temporal coherence represents a significant bottleneck distinct from computational complexity. Using a Large Language Model to estimate the 'effective time' (a proxy for temporal coherence) needed for humans to complete remote O*NET tasks, the study reveals a non-linear link between AI coherence and automation potential. A key finding is that an 8-hour coherence capability could potentially automate around 80-84\% of the analyzed remote tasks.

Read More

This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.