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

TL;DR

This paper presents a novel approach to operationalize diversity and inclusion requirements in AI systems by developing a tailored user story template, conducting focus groups, and leveraging large language models to generate synthetic D&I requirements.

Contribution

It introduces a new method for eliciting and capturing D&I requirements in AI development through a specialized user story template and empirical validation with focus groups and language models.

Findings

Effective D&I requirements can be captured using the proposed user story template.

Focus groups help refine D&I requirements for specific AI systems.

Large Language Models can generate synthetic D&I requirements for AI systems.

Abstract

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.