AI for All: Operationalising Diversity and Inclusion Requirements for AI Systems
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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.
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
9 of 32 references.
References · 9
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
- 2017Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraintscited by 420
- 2019Principles alone cannot guarantee ethical AIcited by 135
- 2019The Ethics of AI Ethics -- An Evaluation of Guidelinescited by 129
- 2022Requirements Engineering for Artificial Intelligence Systems: A Systematic Mapping Studycited by 14
- 2023Diversity and Inclusion in Artificial Intelligencecited by 14
- 2023Requirements Practices and Gaps When Engineering Human-Centered Artificial Intelligence Systemscited by 8
- 2020Data, Power and Bias in Artificial Intelligencecited by 7
- 2023Challenges and Solutions in AI for Allcited by 3
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Citing papers per year: 2023: 2; 2024: 1; 2025: 3.
6 papers.
- 2023Investigating Responsible AI for Scientific Research: An Empirical Studycited by 4
- 2025Leveraging LLMs for User Stories in AI Systems: UStAI Datasetcited by 4
- 2025Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Modelscited by 3
- 2023A Vision for Operationalising Diversity and Inclusion in AIcited by 2
- 2024Diversity and Inclusion in AI for Recruitment: Lessons from Industry Workshopcited by 1
- 2025A Question Bank to Assess AI Inclusivity: Mapping out the Journey from Diversity Errors to Inclusion Excellencecited by 0
- 2025A Question Bank to Assess AI Inclusivity: Mapping out the Journey from Diversity Errors to Inclusion Excellence23 Jun 2025
- 2025Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models23 Apr 2025
- 2025Leveraging LLMs for User Stories in AI Systems: UStAI Dataset1 Apr 2025
- 2024Diversity and Inclusion in AI for Recruitment: Lessons from Industry Workshop9 Nov 2024
- 2023Investigating Responsible AI for Scientific Research: An Empirical Study15 Dec 2023
- 2023A Vision for Operationalising Diversity and Inclusion in AI11 Dec 2023