Learning Paradigms for Hybrid Decision-Making Systems

The rapid integration of AI systems into high-stakes domains has revealed persistent issues of user distrust, algorithmic aversion, and over-reliance, highlighting the need for decision-making frameworks in which humans and machines synergistically collaborate towards the solution of the task. Hybrid Decision-Making Systems (HDMS) have emerged as a paradigm where humans and AI jointly contribute to the same task, leveraging and integrating human strengths like domain expertise, contextual understanding and flexible reasoning, alongside machines’ computational power. This survey offers a structured overview of learning paradigms for HDMS, with a particular focus on uncertainty-driven abstention mechanisms, which determine when an AI system should act autonomously or when it should call for human intervention. We formalise and compare algorithmic approaches that embed machine learning models with the capacity to “know what they don’t know”, analysing how abstention policies and system architectures integrate human expertise into the decision pipeline. Beyond abstention, we examine frameworks that support direct human–machine interaction during and after the learning process, outlining emerging approaches that foster bidirectional collaboration between humans and AI. Building on this analysis, we propose a taxonomy of three learning paradigms characterising progressively tighter human–machine integration.