Artificial Intelligence Governance of Prescription Opioid Misuse Prediction Models Under Unmeasured Pharmacy Chain Corporate Lobbying Capture of Narcotics Board Scheduling: A Political Hazard Estimation

Prescription opioid misuse prediction models are increasingly proposed as instruments of pharmaceutical governance, yet their validity depends on a scheduling environment that pharmacy chains can lobby to shape. The framework treats scheduling decisions as a latent political variable that simultaneously determines the legal status of compounds, the composition of training data, and the operational thresholds at which prediction models trigger clinical or enforcement action. Because scheduling capture is unmeasured, the paper characterises the resulting bias as a form of omitted-variable confounding that cannot be corrected through conventional model validation. A formal hazard decomposition distinguishes three pathways: label contamination through selective enforcement, covariate shift through formulary substitution, and threshold drift through regulatory reclassification. Governance of AI misuse prediction must therefore extend beyond model auditing to include institutional safeguards on the scheduling process itself. The contribution is a conceptual and methodological framework that reframes AI governance in pharmaceutical policy as a problem of political measurement rather than technical accuracy alone.