Managing operational and compliance risks across a large, diverse supplier base is increasingly complex. Traditional audit-based approaches are resource-intensive and limited in scope, making a risk-based strategy essential to focus attention where potential issues are most likely to arise. To address this challenge, Amazon developed PRISM AI (Predictive Risk Intelligence for Supplier Management), an interpretable machine learning system that predicts and explains supplier-level risk across global supply chains. Trained on tens of thousands of audit and assessment records, PRISM integrates multiple data sources including self-assessment questionnaires, incident reports, external media signals, and geo-sector indicators. These inputs enable near–real-time identification of elevated risk patterns and emerging concerns across supplier networks. The model supports suppliers with varying data availability—those with extensive records, limited information, or none—by combining transfer learning, rule-based heuristics, and domain-specific indicators. Each prediction is accompanied by transparent attribution, showing which factors, such as certification gaps or regional exposure, most influenced the score. Built with monotonic constraints, the system ensures logically consistent and explainable outputs suitable for regulatory and operational contexts.

This demo provides NeurIPS participants with a hands-on view of how AI research can be operationalized for large-scale, real-world impact. PRISM helps compliance teams prioritize reviews, streamline supplier onboarding, and enhance oversight efficiency. For researchers, it illustrates techniques for building interpretable models under data imbalance and for integrating structured and unstructured signals. For practitioners, it demonstrates how AI can advance responsible sourcing and sustainability objectives across complex global ecosystems.