AI alignment boundaries

AI alignment boundaries | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 3 September 2025 V2 Latest version Share on AI alignment boundaries Author : Konstantyn Spasokukotskiy 0009-0002-3753-0905 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.171697103.39692698/v2 805 views 357 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract 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. Supplementary Material File (ai alignment boundaries 15062024.pdf) Download 6.72 MB Information & Authors Information Version history V1 Version 1 29 May 2024 V2 Version 2 03 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords ai alignment algorithmic limits alignment threshold safety constraints Authors Affiliations Konstantyn Spasokukotskiy 0009-0002-3753-0905 [email protected] anonymous View all articles by this author Metrics & Citations Metrics Article Usage 805 views 357 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Konstantyn Spasokukotskiy. AI alignment boundaries. Authorea . 03 September 2025. DOI: https://doi.org/10.22541/au.171697103.39692698/v2 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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