AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems
This work addresses the need for a structured classification in the domain of human-AI interaction, but it is incremental as it synthesizes existing literature rather than introducing new methods.
The paper tackles the problem of sparse and varied classifications for human interaction with machine learning systems by proposing a taxonomy of Hybrid Decision Making Systems, providing a conceptual and technical framework to understand current literature.
Everyday we increasingly rely on machine learning models to automate and support high-stake tasks and decisions. This growing presence means that humans are now constantly interacting with machine learning-based systems, training and using models everyday. Several different techniques in computer science literature account for the human interaction with machine learning systems, but their classification is sparse and the goals varied. This survey proposes a taxonomy of Hybrid Decision Making Systems, providing both a conceptual and technical framework for understanding how current computer science literature models interaction between humans and machines.