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

arXiv: https://arxiv.org/abs/2506.18538 · HTML: https://mlsanity.com/paper/2506.18538/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 23 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Question-Driven Design Process for Explainable AI User Experiences](https://mlsanity.com/paper/2104.03483/) (2021)
- [Diversity and Inclusion in Artificial Intelligence](https://mlsanity.com/paper/2305.12728/) (2023)
- [Identifying Explanation Needs of End-users: Applying and Extending the XAI Question Bank](https://mlsanity.com/paper/2307.09369/) (2023)
- [AI for All: Operationalising Diversity and Inclusion Requirements for AI Systems](https://mlsanity.com/paper/2311.14695/) (2023)
- [QB4AIRA: A Question Bank for AI Risk Assessment](https://mlsanity.com/paper/2305.09300/) (2023)
- [What Does a Software Engineer Look Like? Exploring Societal Stereotypes in LLMs](https://mlsanity.com/paper/2501.03569/) (2025)
- [Validating Simulations of User Query Variants](https://mlsanity.com/paper/2201.07620/) (2022)
- [AI for All: Identifying AI incidents Related to Diversity and Inclusion](https://mlsanity.com/paper/2408.01438/) (2024)
- [Responsible AI Question Bank: A Comprehensive Tool for AI Risk Assessment](https://mlsanity.com/paper/2408.11820/) (2024)
- [Diversity and Inclusion in AI for Recruitment: Lessons from Industry Workshop](https://mlsanity.com/paper/2411.06066/) (2024)

## Abstract (arXiv)

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.