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

Authors

  • William F. Lawless Paine College
  • Ranjeev Mittu Naval Research Laboratory
  • Don Sofge Naval Research Laboratory
  • Laura Hiatt Naval Research Laboratory

DOI:

https://doi.org/10.1609/aimag.v40i3.2866

Abstract

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.

Additional Files

Published

2019-07-09

How to Cite

Lawless, W. F., Mittu, R., Sofge, D., & Hiatt, L. (2019). Artificial intelligence, Autonomy, and Human-Machine Teams — Interdependence, Context, and Explainable AI. AI Magazine, 40(3), 5-13. https://doi.org/10.1609/aimag.v40i3.2866

Issue

Vol. 40 No. 3: Fall 2019

Section

Editorials

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