A Question Bank to Assess AI Inclusivity: Mapping out the Journey from Diversity Errors to Inclusion Excellence

2025-06-23

AI Summary

Existing AI risk assessment frameworks lack systematic consideration of diversity and inclusion (D&I) and suffer from a dearth of actionable, standardized evaluation tools. Method: We propose the first comprehensive, lifecycle-spanning inclusive assessment framework for AI, structured around five pillars—human, data, process, system, and governance—and comprising a curated repository of 253 standardized questions. Our methodology integrates systematic literature review, analysis of D&I guidelines and responsible AI frameworks, and an innovative validation approach employing 70 AI-generated personas to simulate diverse user perspectives. Contribution/Results: Empirically validated across multiple roles and real-world scenarios, the framework significantly enhances the operationalizability and contextual adaptability of D&I assessments. It provides researchers, developers, and policymakers with a theoretically rigorous yet practice-oriented toolset, thereby advancing equitable, trustworthy AI governance.

Technology Category

• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Ensuring diversity and inclusion (D&I) in artificial intelligence (AI) is crucial for mitigating biases and promoting equitable decision-making. However, existing AI risk assessment frameworks often overlook inclusivity, lacking standardized tools to measure an AI system's alignment with D&I principles. This paper introduces a structured AI inclusivity question bank, a comprehensive set of 253 questions designed to evaluate AI inclusivity across five pillars: Humans, Data, Process, System, and Governance. The development of the question bank involved an iterative, multi-source approach, incorporating insights from literature reviews, D&I guidelines, Responsible AI frameworks, and a simulated user study. The simulated evaluation, conducted with 70 AI-generated personas related to different AI jobs, assessed the question bank's relevance and effectiveness for AI inclusivity across diverse roles and application domains. The findings highlight the importance of integrating D&I principles into AI development workflows and governance structures. The question bank provides an actionable tool for researchers, practitioners, and policymakers to systematically assess and enhance the inclusivity of AI systems, paving the way for more equitable and responsible AI technologies.

Problem

• Lack of standardized tools to measure AI inclusivity
• Existing frameworks overlook D&I principles in AI
• Need for actionable assessment of AI system inclusivity

Innovation

• Structured AI inclusivity question bank with 253 questions
• Iterative multi-source approach for question bank development
• Simulated evaluation with 70 AI-generated diverse personas