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Responsible AI Question Bank for Risk Assessment

delete2026-05-23
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PRE
AI
S
Sung Une Lee *
H
Harsha Perera
L
Lie, Yue
B
Boming Xia
Q
Qinghua Lu
L
Liming Zhu
O
Olivier Salvado
J
Jon Whittle
DOI:10.1145/3790096delete
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Abstract

Abstract

En 中文
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.
Keywords:
Artificial intelligence
responsible AI
AI ethics
risk assessment

Journal

ACM Computing Surveys cover
ACM Computing Surveys
IF:
28
Papers:
2.4K
Citations:
3.5W

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A
australian national university
Scholars:
1.9K
Papers: 1.0K
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R
royal melbourne institute of technology (rmit)
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Papers: 334
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C
CSIRO Data61
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52
Papers: 38
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A
adelaide university
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3.5K
Papers: 1.6K
Citations: 1
U
university of adelaide
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1.1K
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Citations: 0
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