Geopolitical Risk Mitigation in Information Governance:An Evaluation Framework for Algorithmic Bias and Data Security in AI-DrivenGlobal Supply Chains

Abstract

Artificial intelligence (AI) is increasingly considered a cornerstone of contemporary discourse concerning geopolitics and geoeconomics.The domain appears to be largely constrained by a teleological, zero-sum narrative that prioritizes the question of which state will secure AI supremacy in this great power competition.This perspective diverts attention from a more critical and empirically accessible phenomenon that is the increasing utilization of artificial intelligence, data analytics and machine learning as fundamental instruments for geopolitical risk mitigation, supply chain oversight and state-led regulatory enforcement within an increasingly fragmented and competitive global economic landscape. This paper reconceptualises AI-driven supply chain risk management systems not merely as operational tools, but as critical sites of algorithmic governance where technical properties such as bias detection mechanisms, data security protocols, and cross-jurisdictional information routing function as direct variables of state power. By integrating structural realism with power transition theory, this study moves beyond aggregate AI capability metrics to examine the systemic institutionalisation of these technologies. The study employs a multidimensional evaluation framework that positions algorithmic bias exposure, data security, structural dependency concentration, and regulatory fragmentation risk as fundamental determinants of geopolitical leverage,implicating how automated systems actively reshape the global distribution of risk, information, and strategic control.To conceptualize the structural friction inherent in global digital supply chains, this paper introduces the 'algorithmic sovereignty trilemma.' This construct demonstrates why technical optimisation, data sovereignty, and cross-bloc interoperability cannot be simultaneously maximised, effectively mapping the fundamental trade-offs states must navigate amidst technological decoupling. The framework is applied illustratively to the semiconductor and critical-minerals supply chains implicated in the ongoing US-China technological rivalry, and is situated against the European Union’s AI Act, United States export-control practice, and China’s data-security architecture. The paper argues that algorithmic supply chain governance is best understood not as a neutral optimisation problem but as a site where distributional power, sovereignty claims, and systemic risk are jointly produced.The paper concludes that by foregrounding the technical architecture of supply chain oversight, states can better anticipate how algorithmic dependencies amplify security dilemmas and harden geopolitical fissures .It suggests a recalibration of national policy toward the creation of resilient, audit-transparent oversight architectures that mitigate asymmetric vulnerabilities and prevent the weaponization of critical data-flow interdependencies. By integrating technological expertise with systemic structural analysis, the research underscores how the securitization of AI value chains inevitably promotes the emergence of techno-blocs, which utilize exclusive infrastructure partnerships to consolidate authority over global digital inputs.