Responsible AI Question Bank for Risk Assessment

Abstract

The rapid growth of AI underscores the need for responsible AI (RAI) practices. While many RAI checklists and frameworks exist, practitioners still struggle with how to use them in practice across roles and stages. We introduce the RAI Question Bank, a role-tagged and lifecycle-tagged, evidence-oriented question set that simplifies interaction for executives, managers, and developers while preserving comprehensive coverage mapped to leading frameworks and regulations (e.g., EU AI Act). With comprehensive taxonomy and linkage between lower-level questions and higher-level themes, the Question Bank facilitates cohesive assessments. Two case studies show how it surfaces risks, prioritizes effort, and supports policy alignment.

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