Abstract

As artificial intelligence (AI) algorithms becomes increasingly embedded in workplace decision-making, its use in promotion decisions remains both promising and contested. While AI algorithms have been widely adopted in hiring, promotion decisions present distinct challenges: they are subjective, relational, and socially embedded. This thesis investigates how trust in AI algorithms unfolds in the context of promotion decisions by examining the perceptions of professionals across three roles: developers, users, and consultants. Through a mixed-methods approach combining qualitative interviews and quantitative surveys, the study explores both how AI algorithms are used and perceived in promotion contexts and what factors shape trust in its recommendations. Drawing on the Integrative Model of Organizational Trust, the study identifies ability, integrity, and transparency as antecedents of trust, while highlighting the influence of role-specific experience. The findings indicate that no single predictor of trust was sufficient to sustain trust in AI algorithms when they contradicted human judgment. Instead, participants preferred collaborative decision-making, where AI algorithms augment rather than replace human insight. Theoretical contributions include situating trust in AI algorithms within the underexplored context of promotion and offering a multi-role perspective. Practical implications point to the need for participative design, targeted training, and trust-preserving collaboration between human decision-makers and AI algorithms. Overall, this thesis provides conceptual and actionable insight into how organizations can responsibly integrate AI algorithms into promotion practices while maintaining trust.