To Impress an Algorithm: Minoritized Applicants’ Perceptions of Fairness in AI Hiring Systems

Technology firms increasingly leverage artificial intelligence (AI) to enhance human decision-making processes in the rapidly evolving talent acquisition landscape. However, the ramifications of these advancements on workforce diversity remain a topic of intense debate. Drawing upon Gilliland’s procedural 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 Latinx or Hispanic undergraduates specializing in computer and information science to discern how gender, race, and ethnicity influence attitudes toward AI in hiring. 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.