AI alignment boundaries

This paper presents a theoretical inquiry into the domain of artificial intelligence (AI), aiming to delineate the boundaries within which an AI system maintains its benign nature. The boundaries are assessed by integrating a set of AI alignment constraints, sourced from algorithmic principles and societal power distribution. Given the diverse nature of these phenomena, a proxy measure is employed to ensure comparability. Cognitive task complexity serves as the standardization metric, which maps heterogene domains onto a unified scale. The analysis spans prevalent algorithmic techniques aimed at achieving alignment. It reveals their potential for safe AI operations. Moreover, the analysis yields an observation that the boundaries of AI alignment constitute a distinct data pattern. It can be regularized and extrapolated. Consequently, a criterion for enhanced alignment is proposed. It breeds a new class of AI alignment, characterized by fail-safety across all actual cognitive tasks. An algorithm feature to implement the alignment class is proposed, contributing to the advancement of AI safety and alignment research.