Responsible AI Question Bank: A Comprehensive Tool for AI Risk Assessment
Sung Une Lee, Harsha Perera, Yue Liu, Boming Xia, Qinghua Lu, Liming, Zhu, Olivier Salvado, Jon Whittle

TL;DR
This paper introduces the RAI Question Bank, a comprehensive tool integrating ethics principles into a structured framework to assess AI risks, improve governance, and ensure compliance with emerging regulations.
Contribution
The study presents the first systematic, question-based framework linking AI ethics principles to risk assessment, enhancing cohesive evaluation and regulatory compliance.
Findings
Effective in identifying potential AI risks
Supports compliance with regulations like the EU AI Act
Facilitates trustworthy AI development
Abstract
The rapid growth of Artificial Intelligence (AI) has underscored the urgent need for responsible AI practices. Despite increasing interest, a comprehensive AI risk assessment toolkit remains lacking. This study introduces our Responsible AI (RAI) Question Bank, a comprehensive framework and tool designed to support diverse AI initiatives. By integrating AI ethics principles such as fairness, transparency, and accountability into a structured question format, the RAI Question Bank aids in identifying potential risks, aligning with emerging regulations like the EU AI Act, and enhancing overall AI governance. A key benefit of the RAI Question Bank is its systematic approach to linking lower-level risk questions to higher-level ones and related themes, preventing siloed assessments and ensuring a cohesive evaluation process. Case studies illustrate the practical application of the RAI…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Explainable Artificial Intelligence (XAI)
