MACHIV
MACHIV is a theoretical framework in the study of human–machine collaboration that investigates how machine intelligence can augment human decision making and perceptual tasks in complex environments. The term is used in academic literature to describe a family of approaches that combine automated inference, data visualization, and interactive planning to support experts rather than replace them.
Key principles include mixed-initiative interaction, where humans and machines share control; interpretability and transparency of machine
Common methods encompass hybrid AI planning, explainable AI interfaces, and collaborative dashboards that enable preference elicitation,
Critics caution that MACHIV systems can introduce new forms of bias, overreliance, or opacity if explanations