Artificial intelligence (AI) is increasingly emerging as a decision-making entity in our daily lives. Yet, individual responses to human (HDM) as compared to AI-based, algorithmic decision-making (ADM) are still poorly understood and prior findings are inconsistent. This highlights the importance of more comparative assessments of ADM and HDM prior to the implementation of corresponding AI-based decision-making processes. Here, we plan on expanding the Stimulus-Organism-Response framework to ADM versus HDM in the context of loan applications. We will manipulate whether a human agent or an AI supposedly decides on participants’ (hypothetical) loan applications and assess perceived fairness, acceptance, and adoption of the corresponding decision-making system. Moreover, we will manipulate distributive fairness on an individual (positive vs. negative personal outcomes; Exp. 1) and group level (biased vs. unbiased overall distribution; Exp. 2). Prior research suggests that outcome favourability crucially impacts individual perception of decision-making processes irrespective of the decision-making mode. We expect that an HDM-mode as compared to an ADM-mode will lead to higher acceptance and adoption rates. Furthermore, we expect this effect to be impacted by participants’ trust and distributive fairness perception. Our research aims to comparatively assess the mechanisms behind the perception and subsequent acceptance and adoption of ADM as compared to HDM systems.