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Responsible AI Question Bank for Risk Assessment

Year: 2026

Type: article

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.

Source: ACM Computing Surveys

DOI: 10.1145/3790096

Authors: Sung Une Lee, Harsha Perera, Yue Liu, Boming Xia, Qinghua Lu, Liming Zhu, Olivier Salvado, Jon Whittle

Institutions: Commonwealth Scientific and Industrial Research Organisation, Data61, Deakin University, RMIT University, Australian National University, Adelaide University, The University of Adelaide, Queensland University of Technology, The University of Melbourne

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

OpenAlex ID: W7126079401

FWCI: 24.25

Cites: 30

Cited by: 4

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