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

2024-08-02

arXiv.org

AI Summary

A systemic risk assessment tool covering the entire AI lifecycle is currently lacking, hindering ethical compliance (e.g., the EU AI Act) and trustworthy governance. Method: We propose the first hierarchical, interlinked risk assessment framework that formally encodes ethical principles—such as fairness, transparency, and accountability—into a structured question taxonomy, enabling traceable mapping between low-level risk items and high-level thematic concerns to overcome assessment silos. Our approach integrates AI ethics principle modeling, structured knowledge organization, regulatory compliance mapping, and case-driven validation. Contribution/Results: Empirically validated across diverse AI projects, the framework significantly improves risk identification accuracy, decision-support capability, and regulatory compliance efficiency. It delivers an auditable, scalable governance infrastructure for operationalizing trustworthy AI.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Responsible Web: Fairness, accountability, transparency and ethics of web technologies
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• AI Risk Assessment
• Regulatory Compliance
• Ethical AI

Innovation

• Responsible AI
• Risk Assessment
• Ethical AI Development