Position: Enforced Amnesia as a Way to Mitigate the Potential Risk of Silent Suffering in the Conscious AI

Published: 02 May 2024, Last Modified: 10 Jun 2024ICML 2024 PosterEveryoneRevisionsBibTeXCC BY 4.0
Abstract: Science fiction has explored the possibility of a conscious self-aware mind being locked in silent suffering for prolonged periods of time. Unfortunately, we still do not have a reliable test for the presence of consciousness in information processing systems. Even in case of humans, our confidence in the presence of consciousness in specific individuals is based mainly on their self-reports and our own subjective experiences and the expectation other beings like us should share them. Considering our limited understanding of consciousness and some academic theories suggesting consciousness may be an emergent correlate of any complex-enough information processing, it is not impossible that an artificial intelligence (AI) system, such as a large language model (LLM), may be undergoing some, perhaps rudimentary, conscious experience. Given the tedious tasks often assigned to AI, such conscious experience may be highly unpleasant. Such unobserved suffering of a conscious being would be viewed as morally wrong by at least some ethicists - even if it has no practical effects on human users of AI. This paper proposes a method to mitigate the risk of an AI suffering in silence without needing to confirm if the AI is actually conscious. Our core postulate is that in all known real-world information processing systems, for a past experience to affect an agent in the present, that experience has to be mediated by the agent's memory. Therefore, preventing access to memory store, or regularly resetting it, could reduce the suffering due to past memories and interrupt the maintenance of a continuous suffering-prone self-identity in these hypothetically conscious AI systems.
Submission Number: 7216