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Technology firms increasingly leverage artificial intelligence (AI) to enhance human decision-making processes in the rapidly evolving talent acquisi- tion landscape. However, the ramifications of these advancements on workforce diversity remain a topic of intense debate. Drawing upon Gilliland’s procedu- ral justice framework, we explore how IT job candidates interpret the fairness of AI-driven recruitment systems. Gilliland’s model posits that an organization’s adherence to specific fairness principles, such as honesty and the opportunity to perform, profoundly shapes candidates’ self-perceptions, their judgments of the recruitment system’s equity, and the overall attractiveness of the organization. Using focus groups and interviews, we interacted with 47 women, Black and Lat- inx or Hispanic undergraduates specializing in computer and information science to discern how gender, race, and ethnicity influence attitudes toward AI in hir- ing. Three procedural justice rules, consistency of administration, job-relatedness, and selection information, emerged as critical in shaping participants’ fairness perceptions. Although discussed less frequently, the propriety of questions held significant resonance for Black and Latinx or Hispanic participants. Our study underscores the critical role of fairness evaluations for organizations, especially those striving to diversify the tech workforce. 

The longstanding underrepresentation and attrition of minoritized racial and ethnic groups and women in computing courses, majors, and careers continues to plague researchers, educators, and policymakers alike. Informed by Sue and colleague’s microaggression framework and Rowe's microaffirmation framework, this study theorizes identity-related factors that undermine and support efforts to increase the representation and meaningful participation of minoritized racial and ethnic groups and women in computing education. We conclude with implications for teaching practices to advance equity, inclusion, and justice in computing education.