David Kaber

David B. Kaber is an American human factors engineer and professor whose research on supervisory control, automation, and human-system interaction has shaped the theoretical foundations of out-of-the-loop unfamiliarity and adaptive automation. Working with Mica Endsley and others, Kaber developed formal models of human-automation interaction that treat the operator not as a passive monitor but as an adaptive agent whose cognitive state — attention, workload, situation awareness — must be continuously matched to the demands of the task.

Kaber's most significant contribution is the development of quantitative frameworks for adaptive automation allocation — the dynamic assignment of tasks between human and machine based on real-time assessment of operator state. This work addresses the central paradox of supervisory control: the moment when the human is most needed (system failure, high uncertainty) is precisely the moment when the human, having been out of the loop, is least prepared to act. Kaber's models attempt to predict when the human should be brought back into the loop, and what information must be presented to restore situation awareness before control is transferred.

The practical challenge is that adaptive automation requires the system to model the human's cognitive state in real time, and any such model is necessarily incomplete. Kaber's work acknowledges this limitation and argues for conservative automation — systems that err on the side of keeping the human engaged rather than systems that optimize for efficiency by removing the human from all routine tasks.