AI Epidemiology: achieving explainable AI through expert oversight patterns

TL;DR

AI Epidemiology introduces a population-level surveillance framework for governing AI systems, using expert interaction patterns and statistical associations to ensure reliability and interpretability without burdening experts.

Contribution

The paper presents a novel framework that standardizes expert-AI interactions and applies epidemiological methods for AI governance, bypassing complex interpretability challenges.

Findings

Standardized expert interaction data predicts AI output failures.

Framework provides automatic audit trails and reliability scores.

Enables domain experts to oversee AI without ML expertise.

Abstract

AI Epidemiology is a framework for governing and explaining advanced AI systems by applying population-level surveillance methods to AI outputs. The approach mirrors the way in which epidemiologists enable public health interventions through statistical evidence before molecular mechanisms are understood. This bypasses the problem of model complexity which plagues current interpretability methods (such as SHAP and mechanistic interpretability) at the scale of deployed models. AI Epidemiology achieves this population-level surveillance by standardising capture of AI-expert interactions into structured assessment fields: risk level, alignment score, and accuracy score. These function as exposure variables which predict output failure through statistical associations, much like cholesterol and blood pressure act as exposure variables predicting cardiac events. Output-failure associations are subsequently validated against expert overrides and real-world outcomes. The framework places zero burden on experts and provides automatic audit trails by passively tracking expert convergence and divergence with AI recommendations. Since it analyses outputs rather than internal model computations, it also provides governance continuity when institutions update models and switch vendors. Finally, by providing reliability scores and semantic assessments (e.g. 'this recommendation resembles 500 cases overridden by experts due to guideline violations'), it enables experts and institutions to detect unreliable AI outputs before they cause harm. This democratises AI oversight by enabling domain experts to govern AI systems without requiring machine learning expertise.