Dynamic Equilibrium Theory for Ethical AI: Balancing Epistemic Uncertainty, Human Autonomy, and Social Equity in High-Stakes Fluctuational Decision Systems

This thesis presents a novel theoretical framework for addressing one of the most pressing challenges in contemporary artificial intelligence: how fluctuational AI-driven decision systems can ethically balance epistemic uncertainty, human autonomy, and social equity in high-stakes environments. Current approaches to AI ethics treat these three dimensions as separate, static concerns to be optimized independently. However, this research demonstrates that in fluctuational AI systems operating in critical domains such as healthcare, criminal justice, and financial services, these elements exist in a state of dynamic tension where changes in one dimension necessarily affect the others. The central contribution of this work is the Dynamic Equilibrium Theory (DET), which introduces the concept of fluctuational ethics—the idea that ethical AI systems must be designed to handle not just uncertainty in data or predictions, but uncertainty in the ethical landscape itself. The framework proposes a triadic model where epistemic uncertainty, human autonomy, and social equity form an interconnected system that must maintain dynamic equilibrium rather than static optimization. Through comprehensive analysis of empirical data from real-world AI deployments, including the COMPAS criminal justice algorithm and healthcare diagnostic systems, this research provides quantitative evidence for the interdependence of these ethical dimensions. The ProPublica analysis of COMPAS reveals that Black defendants were 1.96 times more likely to be falsely classified as high risk compared to White defendants, while White defendants were 1.71 times more likely to be incorrectly classified as low risk—demonstrating how epistemic uncertainty, human autonomy, and social equity interact in complex ways that current frameworks fail to address. The thesis develops mathematical formalizations of the dynamic equilibrium using stochastic differential equations and tensor representations, providing a rigorous foundation for implementation. The framework is validated through case studies across multiple high-stakes domains, showing how different contexts require different equilibrium points while maintaining the fundamental integrity of all three dimensions. This work represents a paradigm shift from static, rule-based ethical frameworks to dynamic, adaptive systems that recognize the inherent fluctuational nature of AI decision-making. The implications extend beyond technical implementation to policy and governance, suggesting new approaches to AI regulation that account for temporal ethical dynamics and contextual sensitivity.