ALGORITHMIC TRANSPARENCY, PERCEIVED FAIRNESS & EMPLOYEE TRUST IN AI-DRIVEN HR DECISIONS: A MEDIATION MODEL

Abstract

The algorithmic transparency in AI-driven HR decisions helps employees understand how decisions are made, reducing uncertainty and increasing perceptions of procedural fairness. Perceived fairness acts as a key mediator by translating the transparent AI processes into stronger employee trust in automated recruitment, evaluation, promotion, and reward decisions. This study examines whether algorithmic transparency builds employee trust in AI-driven HR decisions directly or indirectly through perceived fairness. Drawing on organizational justice theory and trust-in-automation theory, a cross-sectional survey of 322 mid-to-senior managers at the multinational corporations in Pakistan was analyzed using Hayes' PROCESS Macro (Model 4) with 5,000 bootstrap resamples. Transparency was positively associated with both trust (β = 0.217, p < .001) and perceived fairness (β = 0.351, p < .001), and fairness was positively associated with trust (β = 0.295, p < .001). The indirect effect of transparency on trust through fairness was significant (β = 0.103, 95% CI (0.067, 0.142), with the direct effect remaining significant, signifying partial mediation. Results show transparency alone is insufficient to secure the employee trust; its impact depends on whether decisions are perceived as fair, with the implications for designing more trustworthy AI- driven HR systems.