# AI for All: Operationalising Diversity and Inclusion Requirements for AI Systems

arXiv: https://arxiv.org/abs/2311.14695 · HTML: https://mlsanity.com/paper/2311.14695/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 7 Nov 2023.

## Builds on (most cited first, 9 of 9)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints](https://mlsanity.com/paper/1707.09457/) (2017)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Requirements Engineering for Artificial Intelligence Systems: A Systematic Mapping Study](https://mlsanity.com/paper/2212.10693/) (2022)
- [Diversity and Inclusion in Artificial Intelligence](https://mlsanity.com/paper/2305.12728/) (2023)
- [Requirements Practices and Gaps When Engineering Human-Centered Artificial Intelligence Systems](https://mlsanity.com/paper/2301.10404/) (2023)
- [Data, Power and Bias in Artificial Intelligence](https://mlsanity.com/paper/2008.07341/) (2020)
- [Challenges and Solutions in AI for All](https://mlsanity.com/paper/2307.10600/) (2023)

## Built on by (most cited first, 6 of 6)

- [Investigating Responsible AI for Scientific Research: An Empirical Study](https://mlsanity.com/paper/2312.09561/) (2023)
- [Leveraging LLMs for User Stories in AI Systems: UStAI Dataset](https://mlsanity.com/paper/2504.00513/) (2025)
- [Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models](https://mlsanity.com/paper/2504.16969/) (2025)
- [A Vision for Operationalising Diversity and Inclusion in AI](https://mlsanity.com/paper/2312.06074/) (2023)
- [Diversity and Inclusion in AI for Recruitment: Lessons from Industry Workshop](https://mlsanity.com/paper/2411.06066/) (2024)
- [A Question Bank to Assess AI Inclusivity: Mapping out the Journey from Diversity Errors to Inclusion Excellence](https://mlsanity.com/paper/2506.18538/) (2025)

## Abstract (arXiv)

As Artificial Intelligence (AI) permeates many aspects of society, it brings numerous advantages while at the same time raising ethical concerns and potential risks, such as perpetuating inequalities through biased or discriminatory decision-making. To develop AI systems that cater for the needs of diverse users and uphold ethical values, it is essential to consider and integrate diversity and inclusion (D&I) principles throughout AI development and deployment. Requirements engineering (RE) is a fundamental process in developing software systems by eliciting and specifying relevant needs from diverse stakeholders. This research aims to address the lack of research and practice on how to elicit and capture D&I requirements for AI systems. We have conducted comprehensive data collection and synthesis from the literature review to extract requirements themes related to D&I in AI. We have proposed a tailored user story template to capture D&I requirements and conducted focus group exercises to use the themes and user story template in writing D&I requirements for two example AI systems. Additionally, we have investigated the capability of our solution by generating synthetic D&I requirements captured in user stories with the help of a Large Language Model.