Under the Hood: Scaling Responsible AI at Uber

This post details the evolution and operationalization of Uber's Responsible AI program. Key technical contributions include the development of a centralized Model Catalog with integrated Model Cards, automated population of metadata, and integration into the ML development workflow. Explainability is enhanced through the integration of Permutation Feature Importance (PFI) for global attributions and SHAP/Integrated Gradients for local attributions, directly linked to Model Cards. Governance is operationalized by embedding checks into PRD/ERD reviews and implementing pre-deployment Model Card completion as an auditable gating mechanism. The program emphasizes a shift-left approach, adaptive controls, and system-level visibility for AI governance.