Refining Ethical AI: The Impact of AI Fairness on Employee Learning from AI

Understanding how to foster value-aligned human-AI collaboration has become critical for organizational digital transformation. Grounded in AI value alignment theory, this study examines how AI fairness enhances employee learning from AI by improving both AI utility and AI trust, with AI job role clarity serving as a moderating factor. Employing a multi-wave survey, this research demonstrates three key findings. First, AI fairness significantly enhances employee learning from AI. Second, this impact occurs through dual pathways. The technical pathway demonstrates that algorithmic transparency and output reliability improve AI utility, thereby enabling employees to recognize the instrumental value of AI in knowledge acquisition and innovation. The normative pathway reveals that participation rights and non-discriminatory decisions strengthen employees’ trust in AI, fostering their perception of AI as a value-congruent collaborative partner rather than a threat. Third, the higher the level of AI job role clarity, the stronger the mediating effect of AI utility and AI trust in the relationship between AI fairness and employee learning from AI. These findings provide theoretical foundations for developing value-aligned human-AI collaboration frameworks and offer practical insights for facilitating organizational human-AI synergy.