Spider Vision: A Natural Framework for AI Governance

Technical audits evaluate model behavior, robustness, and bias, whereas institutional governance mechanisms separately oversee compliance, accountability, and social impact. When applied in isolation, these approaches fail to detect interaction-driven risks that emerge at the boundary between system behavior and organizational deployment. This paper identifies this persistent coordination failure as a dual oversight gap in contemporary AI governance. To address this gap, the paper introduces Spider Vision, a diagnostic and operational framework inspired by the functional differentiation of spider visual systems. The framework integrates two concurrent oversight lenses: Primary Vision, providing high-resolution monitoring of quantitative model-level metrics such as bias persistence, error propagation, and performance drift; and Secondary Vision, providing wide-angle, low-latency monitoring of observable institutional signals including accountability allocation, incentive misalignment, and trust erosion. Together, these lenses enable structured escalation and adaptive revision without attributing moral agency or intent to AI systems. Spider Vision is normatively grounded in virtue ethics, operationalized as governance standards rather than moral ideals: prudence as anticipatory judgment across interacting risk domains, justice as stabilization of responsibility allocation, and adaptability as dynamic adjustment of oversight parameters in response to empirical feedback. Empirical validation is provided through synthetic simulations across healthcare and autonomous vehicle domains, illustrating reductions in bias persistence (approximately 25%), accountability drift (20%), and trust erosion (15%) relative to single-layer oversight baselines. These results are proof-of-concept and do not claim real-world effect sizes. Positioned against leading governance regimes—including the OECD AI Principles, the EU AI Act, and the NIST AI Risk Management Framework—Spider Vision provides a measurable, operational complement that integrates technical audits and institutional oversight without introducing new principles, regulatory mandates, or ontological claims about AI.