A Taxonomy of Questions for Critical Reflection in Machine-Assisted Decision-Making

Abstract: Decision-makers run the risk of relying too much on machine recommendations, which is associated with lower cognitive engagement. Reflection has been shown to increase cognitive engagement and improve critical thinking and reasoning and therefore decision-making. However, there is currently no approach to support reflection in machine-assisted decision-making. We therefore present a taxonomy that serves to systematically create questions related to machine-assisted decision-making that promote reflection and thus cognitive engagement and ultimately a deliberate decision-making process. Our taxonomy builds on a taxonomy of Socratic questions and a question bank for human-centred explainable AI (XAI), and illustrates how XAI techniques can be utilised and repurposed to formulate questions. As a use case, we focus on clinical decision-making. An evaluation in education confirms the applicability and expected benefits of our taxonomy. Our work contributes to the growing research on human-AI interaction that goes beyond the paradigm of machine recommendations and explanations and aims to enable effective human oversight as required by the European AI Act.