Artificial intelligence (AI) is increasingly used in organizational decision-making, optimizing performance, and cutting operational costs. While AI can potentially improve decision-making processes' efficiency and reliability, empirical research highlights that AI adoption may cause people to question the fairness of algorithmic decisions. Thus, the present study investigates whether distributive and procedural fairness perceptions are influenced by human, algorithmic, and hybrid decision-makers in high versus low task complexity conditions. Participants (N = 391) assessed the perceived distributive and procedural fairness in a pre-registered scenario-based experiment. Decision-maker type (human vs. hybrid vs. AI) and task complexity (low vs. high) were manipulated using a 3x2 between-subject design. It was hypothesized that the human decision-maker would be perceived as fairer than the AI, especially in high-complexity conditions. Furthermore, hybrid decision-makers were hypothesized to be perceived as fairer than AI and human decision-makers in low and high-complexity tasks. The results indicate that people tend to perceive human decision-makers as fairer than AI in situations of high complexity. Additionally, in the high-complexity condition, the hybrid decision-maker was perceived as more distributively fair than AI and less procedural fair than the human decision-maker. In low-complexity tasks, the hybrid decision-maker does not show superiority in fairness perception over AI or humans. Hence, the results support the first hypothesis and contradict the second hypothesis that hybrid decision-makers would be perceived as more distributive and procedural fair than AI and human decision-makers. Implications regarding the consequences of implementing AI in organizational decision-making are discussed, and suggestions for further research are included.