Via Negativa Fairness: A Theology-Informed Framework for Responsible AI

Current responsible artificial intelligence (RAI) frameworks overwhelmingly rely on the assumption that ethical principles—fairness, transparency, accountability—can be positively and completely specified. This assumption is demonstrably false: mathematical impossibility theorems prove that standard fairness definitions are mutually incompatible, and empirical evidence shows that value alignment remains intractable under positive specification alone. We argue that this impasse stems from an unexamined epistemological commitment shared across the RAI literature and propose an alternative grounded in the millennia-old tradition of apophatic (negative) theology. Drawing on three theological traditions—the apophatic via negativa of Pseudo-Dionysius, Maimonides, and Al-Ghaz ¯al¯ı; the Christological concept of kenosis (voluntary self-limitation of power); and the Buddhist principle of prat¯ıtyasamutp ¯ada (dependent origination)—we develop a formal framework called Via Negativa Fairness (VNF). Rather than selecting a single fairness metric to optimise, VNF defines a set of unfairness constraints—outcomes that are demonstrably unjust across reasonable moral disagreement—and proves that the feasible region under these constraints is non-empty under practically relevant conditions. We complement VNF with two architectural design principles: the Kenotic Restraint Principle, which mandates designed self-limitation of AI capabilities independent of risk calculus, and the Relational Audit Protocol, derived from dependent origination, which evaluates bias as an emergent property of system-context interaction rather than a static model attribute. A formal case study on the COMPAS recidivism dataset demonstrates that VNF produces Pareto-dominant outcomes compared to single-metric optimisation, reducing worst-case group disparity by up to 31% while maintaining predictive performance within 2% of unconstrained baselines. This work provides the first rigorous bridge between theological epistemology and machine learning fairness, offering actionable, testable principles that current RAI frameworks lack.