Team Diversity Promotes Software Fairness: An Experiment on Fairness-Aware Requirements Prioritization

Abstract

\textbf{Background:} Fairness and diversity are receiving growing attention in software engineering, particularly as AI and machine learning systems increasingly influence decision-making processes. While fairness is often examined at the algorithmic or data level, there is limited understanding of how it is addressed during the early stages of software development. Moreover, little is known about how team diversity affects fairness-related decisions in software projects. \textbf{Aims:} This study investigates how diversity in software teams influences fairness-aware behavior during requirements prioritization. \textbf{Method:} A controlled experiment was conducted with 27 pairs of software engineering students, including 13 LGBTQ diverse pairs and 14 non diverse pairs. Each pair prioritized user stories with varying fairness implications. Descriptive statistics were used to analyze attitudes and prioritization outcomes, and thematic analysis was applied to examine the reasoning behind participants' decisions. \textbf{Results:} Both groups demonstrated general alignment with fairness principles, prioritizing features that promoted equitable treatment and rejecting those that posed fairness risks. However, LGBTQ diverse pairs were more consistent in rejecting fairness risking stories and made fewer fairness related misprioritization errors. Their reasoning emphasized inclusion, non discrimination, and ethical responsibility, whereas non diverse pairs adopted a more pragmatic, goal oriented perspective. \textbf{Conclusions:} The findings indicate that fairness should be considered from the earliest stages of software development. Team diversity can enhance the identification and interpretation of fairness issues during requirements analysis, fostering more reflective and inclusive decision making.