Tony Rost

Tony Rost

Governance opacity over AI systems shifts in kind as capability asymmetry grows, and the strongest forms defeat the disclosure-based remedies governance ordinarily relies on. This paper applies a six-dimension framework from political theory (legitimacy, accountability, corrigibility, non-domination, subsidiarity, institutional resilience) to six AI governance arrangements already in operation, ordered by increasing capability asymmetry between system and overseer. Proprietary secrecy yields to disclosure at the low end, but at the high end the governed system either games its own evaluation or sits inside the governance process, and transparency remedies lose traction. Legitimacy and non-domination strain more consistently across the sample than corrigibility and resilience, which respond more readily to institutional design quality. The sample cannot separate institutional design maturity from capability asymmetry, and the patterns are offered as hypotheses for multi-rater validation.

Tony Rost

Governance theory has always presumed cognitive comparability between governors and governed. This paper identifies that unstated assumption, constructs a framework that makes it testable, and shows that it is load-bearing. The framework specifies necessary conditions along six dimensions (legitimacy, accountability, corrigibility, non-domination, subsidiarity, and institutional resilience), synthesized from political legitimacy theory, principal-agent models, republican political theory, and AI alignment research. Applied first to existing institutions and then to a prospective case of bounded superintelligent authority where capability asymmetry is radical, the framework finds structural failures on four of six dimensions. Among these, two are design-tractable and two are theory-requiring: the public reason problem under cognitive incomprehensibility and the non-domination problem under permanent capability asymmetry demand genuinely new normative frameworks, not better institutional design. A further finding is that dimensions which function as independent checks under bounded asymmetry become correlated failures under radical asymmetry. The analysis contributes to political theory by exposing foundational assumptions that have gone unexamined because, until now, they have always been satisfied.

Tony Rost

The scientific study of consciousness has begun to generate testable predictions about artificial systems. A landmark collaborative assessment evaluated current AI architectures against six leading theories of consciousness and found that none currently qualifies as a strong candidate, but that future systems might. A precautionary approach to AI sentience, which holds that credible possibility of sentience warrants governance action even without proof, has gained philosophical and institutional traction. Yet existing AI readiness indices, including the Oxford Insights Government AI Readiness Index, the IMF AI Preparedness Index, and the Stanford AI Index, measure economic, technological, and governance preparedness without assessing whether societies are prepared for the possibility that AI systems might warrant moral consideration. This paper introduces the Sentience Readiness Index (SRI), a preliminary composite index measuring national-level preparedness across six weighted categories for 31 jurisdictions. The SRI was constructed following the OECD/JRC framework for composite indicators and employs LLM-assisted expert scoring with iterative expert review to generate an initial dataset. No jurisdiction exceeds ``Partially Prepared'' (the United Kingdom leads at 49/100). Research Environment scores are universally the strongest category; Professional Readiness is universally the weakest. These exploratory findings suggest that if AI sentience becomes scientifically plausible, no society currently possesses adequate institutional, professional, or cultural infrastructure to respond. As a preliminary framework, the SRI provides an initial diagnostic baseline and highlights areas for future methodological refinement, including expanded expert validation, improved measurement instruments, and longitudinal data collection.