AI Safety: A Climb To Armageddon?

The document explores the potential dangers and complexities of implementing safety measures for advanced AI systems, using analogies such as a doomed rock climber and a terrorist attack to illustrate the potential limitations and challenges of AI safety measures.

Abstract

This paper presents an argument that certain AI safety measures, rather than mitigating existential risk, may instead exacerbate it. Under certain key assumptions - the inevitability of AI failure, the expected correlation between an AI system's power at the point of failure and the severity of the resulting harm, and the tendency of safety measures to enable AI systems to become more powerful before failing - safety efforts have negative expected utility. The paper examines three response strategies: Optimism, Mitigation, and Holism. Each faces challenges stemming from intrinsic features of the AI safety landscape that we term Bottlenecking, the Perfection Barrier, and Equilibrium Fluctuation. The surprising robustness of the argument forces a re-examination of core assumptions around AI safety and points to several avenues for further research.

Method

The method of this paper involves presenting a surprising and counterintuitive argument against AI safety, starting from the assumption that AI poses an existential threat to humanity. The paper then shows, under certain assumptions, that safety measures are not only pointless or useless, but actively dangerous. It also explores different strategies for responding to the anti-safety argument, such as Optimism, Mitigation, and Holism, and considers the implications for AI governance and policy measures. The paper aims to challenge conventional assumptions about AI safety and proposes pathways for further research, including developing additional responses and challenging the responses to the counterarguments. Overall, the paper takes a critical and thought-provoking approach to the issue of AI safety.

Main Finding

The main finding of the paper is that, under certain assumptions, safety measures for advanced AI systems are not only ineffective but can also be actively dangerous, challenging the conventional efforts to mitigate the risks of AI and suggesting potential counterintuitive implications for AI safety.

Conclusion

The conclusion of the paper is that, under certain assumptions, safety measures for advanced AI systems may not only be ineffective but could potentially exacerbate the risks, challenging conventional approaches to AI safety and suggesting the need for further research and exploration of response strategies.

Keywords

AI safety research, Optimism, Mitigation, Holism, Bottlenecking, Perfection Barrier, Equilibrium Fluctuation, concrete case studies, empirical evidence, AI governance, policy measures, existential risk, AI capabilities, safety measures, non-deterministic argument, response strategies.