Responsible AI Question Bank: A Comprehensive Tool for AI Risk Assessment

TL;DR

The paper tackles the lack of a comprehensive tool for responsible AI risk assessment by introducing the Responsible AI Question Bank (RAI QB), a hierarchical, principle-driven framework that links low-level risk questions to high-level themes across the AI lifecycle. It presents a five-phase methodology, including systematic literature mapping, question extraction, case studies, iterative improvements, and regulatory alignment with the EU AI Act and ISO 42001, to build a cohesive risk assessment toolkit usable by executives, managers, and developers. Through eight-project case studies and a deep-dive ESG investor framework, the RAI QB demonstrates practical applicability, regulatory compatibility, and the ability to uncover cross-cutting risks such as accountability and transparency while enabling governance and compliance workflows. The work advances AI safety and governance by providing a scalable resource that translates high-level ethics principles into actionable, evidence-based questions and metrics, with future work expanding guidance and quality metrics for broader contexts and ongoing regulatory changes.

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 Question Bank in assessing AI projects, from evaluating risk factors to informing decision-making processes. The study also demonstrates how the RAI Question Bank can be used to ensure compliance with standards, mitigate risks, and promote the development of trustworthy AI systems. This work advances RAI by providing organizations with a valuable tool to navigate the complexities of ethical AI development and deployment while ensuring comprehensive risk management.

Figures (11)

• Figure 1: Research methodology overview.
• Figure 2: The architecture of the question bank.
• Figure 3: The RAI Question Bank categorization, comprising 26 categories and 65 sub-categories, identified from seven AI frameworks.
• Figure 4: Human, Societal, Environmental Wellbeing.
• Figure 5: Human-centred Values.