This paper presents a theoretical inquiry into the domain of artificial intelligence (AI). It investigates a multicameral system, which is a popular scheme in state governance. It investigates the system’s applicability to produce failsafe AI alignment. Decision making by parliaments is often structured as a bicameral system, enabling control of those socio-economic systems, which complexity exceeds complexity of the decision makers. This feature is of interest for the AI-systems that are operating beyond Artificial General Intelligence threshold. The paper answers the question, if AI were organized according to the multicameral governance principle, how far can it be benign. The analysis also elucidates the decision making limits in social systems. The paper hints, if a bicameral parliament were as objective as AI, how complex tasks could be entrusted before the legislative body utterly commits a treason against the constituency. This paper presents a formal information system architecture that mimics the multicameral system. A sobriquet for the architecture is Serial Comptroller Network (SCN). It implements a human-assisted, uninterpretable alignment approach. The discussion focuses on a minimalistic solution within the architectural formalism. An assessment for its digital potential, utility, deficiencies and further improvement options are presented to advance AI research, as well as to aid decision making sophistication in structured socio-economic entities.

This paper presents a theoretical inquiry into the domain of artificial intelligence (AI). It investigates a multicameral system, which is a popular scheme in state governance. It investigates the system’s applicability to produce failsafe AI alignment. Decision making by parliaments is often structured as a bicameral system, enabling control of those socio-economic systems, which complexity exceeds complexity of the decision makers. This feature is of interest for the AI-systems that are operating beyond Artificial General Intelligence threshold. The paper answers the question, if AI were organized according to the multicameral governance principle, how far can it be benign. The analysis also elucidates the decision making limits in social systems. The paper hints, if a bicameral parliament were as objective as AI, how complex tasks could be entrusted before the legislative body utterly commits a treason against the constituency. This paper presents a formal information system architecture that mimics the multicameral system. A sobriquet for the architecture is Serial Comptroller Network (SCN). It implements a human-assisted, uninterpretable alignment approach. The discussion focuses on a minimalistic solution within the architectural formalism. An assessment for its digital potential, utility, deficiencies and further improvement options are presented to advance AI research, as well as to aid decision making sophistication in structured socio-economic entities.

This paper presents a theoretical inquiry into the domain of secure artificial superintelligence (ASI). The paper introduces an architectural pattern tailored to fulfill friendly alignment criteria. Friendly alignment refers to a failsafe artificial intelligence alignment that lacks supervision, while still having a benign effect on humans.  The proposed solution is based on a biomimetic approach to emulate functional aspects of biological consciousness. This approach is aiming to achieve Synthetic Sentiency. It establishes "morality" that secures alignment  in systems of any proportion. This functional feature set is drawn from a cross-section of evolutionary and psychiatric frameworks.  Furthermore, the paper assesses the architectural potential, practical utility, and limitations of this approach. Notably, the architectural pattern supports straightforward implementation by allowing application of simple algorithms, which despite the simplicity can effectively produce an infinite derivative order, directly influencing alignment strength. The alignment strength can be adjusted by manipulating this order, enhancing adaptability and usability of the solution under constraints in practical applications.

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.