The future of human-AI collaboration: a taxonomy of design knowledge for\n hybrid intelligence systems

Abstract

Recent technological advances, especially in the field of machine learning,\nprovide astonishing progress on the road towards artificial general\nintelligence. However, tasks in current real-world business applications cannot\nyet be solved by machines alone. We, therefore, identify the need for\ndeveloping socio-technological ensembles of humans and machines. Such systems\npossess the ability to accomplish complex goals by combining human and\nartificial intelligence to collectively achieve superior results and\ncontinuously improve by learning from each other. Thus, the need for structured\ndesign knowledge for those systems arises. Following a taxonomy development\nmethod, this article provides three main contributions: First, we present a\nstructured overview of interdisciplinary research on the role of humans in the\nmachine learning pipeline. Second, we envision hybrid intelligence systems and\nconceptualize the relevant dimensions for system design for the first time.\nFinally, we offer useful guidance for system developers during the\nimplementation of such applications.\n