AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems
Clara Punzi Roberto Pellungrini Mattia Setzu Fosca Giannotti Dino Pedreschi
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.