Achieving Information Equilibrium: An Integrated Economic-Sociotechnical-Institutional Framework for Governing Artificial Intelligence Asymmetries

Artificial intelligence systems now scores credit, routes trucks, and curates news, yet their decisions remain largely inscrutable to users, regulators, and even the firms that deploy them. Such opacity creates information asymmetries that invite principal–agent conflicts, adverse selection, and moral hazard, undermining both market efficiency and public trust. Existing remedies are fragmented: information-economics studies prescribe incentives but overlook design; sociotechnical research clarifies human–AI interaction but neglects market power; and institutional work codifies norms without detailing engineering trade-offs. This paper weaves those strands into a singleAI Information-Equilibrium Framework (AIIEF). The framework integrates three governance layers. At the micro layer, explainable dashboards, model cards, and user-training loops reveal how an algorithm works and calibrate reliance. At the meso layer, liability clauses, reciprocal data trusts, and tiered transparency labels align incentives so that actors share and use information truthfully. At the macro layer, risk-tiered regulation, auditable standards such as ISO/IEC 42001, and global principles issued by the OECD and UNESCO transform ethical aspirations into enforceable obligations. I show how AIIEF adapts across AI modalities—agentic versus tool-like, large versus small models—and deployment contexts—enterprise versus consumer. I also address common objections—gaming, innovation chill, free-riding, and superficial oversight—by coupling tiered disclosure with regulatory sandboxes, data-trust reciprocity, and decision-right-calibrated explanations. Finally, I outline a research agenda on asymmetry metrics, longitudinal capability evolution, ecosystem simulations, and institutional diffusion, positioning AIIEF as both a cumulative theory for scholars and a pragmatic blueprint for managers.