Artificial Intelligence (AI) is increasingly applied in decision-making processes, yet the influence of procedural and distributive fairness on the perception of AI-supported decisions remains to be further examined. In this vignette study, we aim to systematically manipulate the procedural fairness as well as distributive fairness of allocation processes in different situational scenarios. Procedural fairness will be manipulated by varying the degree of information participants receive about the respective allocation process. Distributive fairness will be manipulated by varying whether the allocation outcome is favourable or unfavourable for participants. Crucially, we compare a condition in which the corresponding allocation process is described as entirely AI-based with a condition in which the same information is gathered, but the allocation process is described as depending on a human decision-maker. We examine the perceived fairness and trust in the respective conditions and their influence on the acceptance and adoption of AI-based versus human decisions. We will further assess the influence of participants’ baseline propensity to trust on their condition-specific perception and evaluation of AI-based versus human decision-making processes. Based on our findings, we aim to contribute to guidelines for designing AI systems that are perceived as fair and trustworthy, enhancing their acceptance to support more efficient decision-making via the usage of AI.