The Epidemiology of Artificial Intelligence

2026-04-15

AI Summary

This study addresses the current lack of an epidemiological framework for assessing population-level exposure to artificial intelligence (AI) and its health impacts. Treating AI as a novel social determinant of health, the work draws on concepts from environmental epidemiology to distinguish between ambient and individual-level AI exposures and proposes, for the first time, a causal inference framework tailored to AI. It further clarifies AI’s role within health-related causal pathways. By integrating conceptual modeling, causal inference theory, and nationally representative survey data from the United States, the study empirically demonstrates the feasibility of this framework. The findings establish a critical methodological foundation for systematically evaluating the long-term population health effects of AI, advancing health equity, and informing responsible AI governance.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: AI for Accessibility
• Machine Learning: Causal Learning

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Artificial intelligence (AI) systems increasingly shape how people access health information, make medical decisions, and receive care -- yet epidemiology lacks frameworks for measuring AI exposure or studying its health effects at the population level. Here we argue that AI now functions as a determinant of health and propose a conceptual framework, borrowed from environmental epidemiology, for studying it. We distinguish ambient AI exposure -- algorithmic curation and AI-mediated institutional decisions that affect populations regardless of individual choice -- from personal AI exposure -- direct, volitional use of AI tools. We characterize AI's possible causal roles in epidemiological models, show that existing experimental approaches are inadequate for capturing chronic, population-level effects, and illustrate these ideas with nationally representative US survey data. We discuss implications for study design, health equity, and AI governance.

Problem

• artificial intelligence
• epidemiology
• health determinants
• population health
• AI exposure

Innovation

• AI exposure
• environmental epidemiology
• health determinants
• algorithmic curation
• population health