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

The pervasive role of Artificial Intelligence (AI) in social interactions, from generating and recommending contents, to Our research methodology encompasses three stages: 1) data processing images and voices, brings numerous benefits but collection and analysis from the published literature on D&I in also necessitates addressing ethical implications and risks, such AI to extract relevant themes, 2) proposing a tailored user story as ensuring equitable and non-discriminatory decision-making, template, and 3) focus group exercise to explore the use of the and preventing the amplification of existing inequalities and extracted themes and user story template to specify D&I biases [1]. Diversity and inclusion (D&I) in AI involves requirements for AI systems. Furthermore, given that involving considering differences and underrepresented perspectives in many stakeholders with diverse attributes in requirements AI development and deployment while addressing potential elicitation is challenging and time-consuming, we decided to biases and promoting equitable outcomes for all concerned explore the utility of Large Language Models in generating user stakeholders [1]. Incorporating D&I principles in AI can enable stories from the D&I in AI themes. After each focus group technology to better respond to the needs of diverse users while exercise, we used GPT-4 to generate D&I user stories. We aimed to examine how closely the user stories from both human 2 Bano et.