Artificial intelligence, Autonomy, and Human-Machine Teams -- Interdependence, Context, and Explainable AI

Because in military situations, as well as for self-driving cars, information must be processed faster than humans can achieve, determination of context computationally, also known as situational assessment, is increasingly important. In this article, we introduce the topic of context, and we discuss what is known about the heretofore intractable research problem on the effects of interdependence, present in the best of human teams; we close by proposing that interdependence must be mastered mathematically to operate human-machine teams efficiently, to advance theory, and to make the machine actions directed by AI explainable to team members and society. The special topic articles in this issue and a subsequent issue of AI Magazine review ongoing mature research and operational programs that address context for human-machine teams. In 1983, William Lawless blew the whistle on Department of Energy (DOE) mismanagement of military radioactive wastes. After his PhD, he joined DOE's citizen advisory board at its Savannah River Site where he coauthored over 100 recommendations on its cleanup.