Speaking fairness: how intercultural communication shapes employee trust in AI-supported hiring

As artificial intelligence (AI) becomes embedded in global talent acquisition, questions of fairness, communication, and cultural interpretation have gained critical importance. This study investigates how intercultural communication styles influence trust and perceived fairness in AI-supported recruitment. Drawing on intercultural pragmatics, attribution theory, and fairness communication frameworks, the research explores how culturally preferred explanation styles—ranging from analytic-transparency to relational-contextual—affect candidate reactions to AI-based hiring decisions. A mixed-methods, cross-national design was adopted, combining a survey experiment (N = 420) across four cultural clusters (Anglo, Confucian, Latin American, and Middle Eastern) with qualitative interviews of 24 HR professionals. Findings reveal that explanation framing significantly moderates perceived fairness: high-context respondents expressed greater trust when messages incorporated empathy and contextual reasoning, whereas low-context participants valued data transparency and procedural detail. Across cultures, perceived fairness predicted organizational trust and employer brand evaluation. The study demonstrates that “speaking fairness” is both a linguistic and cultural act—AI decision-making must be explained through culturally attuned communicative frames. Practically, the findings offer HR professionals a framework for designing AI communication strategies that enhance transparency without violating cultural expectations of respect and relationality. The study contributes to global HRM by integrating intercultural communication theory into the emerging field of algorithmic decision communication.