Fairness, Transparency, and Accountability in AI: A Collectivist Consumer Perspective

This study examines how consumers in a collectivist context perceive and react to algorithmic fairness, transparency, and brand accountability, addressing the Western-centric bias in AI ethics research by focusing on Ghanaian consumers. A conditional process model is developed in which perceived algorithmic fairness predicts perceived brand accountability both directly and indirectly through perceived algorithmic transparency, with collectivist cultural orientation and age moderating the fairness–accountability pathway and its indirect effect through transparency. Using a quantitative cross-sectional survey of 400 consumers in Ghana and structural equation modeling, the findings reveal a reversal of dominant Western assumptions: consumers in this collectivist setting evaluate fairness before transparency, and fairness strongly shapes transparency perceptions. Perceived fairness emerges as a robust predictor of both perceived transparency and brand accountability. Transparency mediates the fairness–accountability relationship, and this indirect effect is amplified among consumers with stronger collectivist orientation and among older respondents. The direct fairness–accountability link is also stronger for these groups, indicating identity-bounded moderated mediation. Theoretically, the study extends justice and attribution theories by demonstrating cultural contingency in the fairness–transparency–accountability sequence. Managerially, results indicate that in collectivist markets, brands should foreground fair treatment before transparency communication and align AI governance with cultural and age-based consumer segments.