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Automation often relies on the human to "jump" into the loop and solve problems when the automation can’t due to conditions outside of which it was designed for or automaton failure. This talk proposes a methodology (Human Autonomy Assistant) to address that situation. Examples are provided from a wild fire context.
Humans are frequently left to “backstop” automated systems, and Human Factors specialists have argued against this for decades with, at best, partial success. What if we took a different tack... and designed to support it? The participants were involved in a recent effort to review and document cases across multiple domains where operators acted as a “failsafe” for automation, intervening in unanticipated situations to maximize success and minimize damage. We defined a “Human As Failsafe” (HAF) incident and then investigated conditions and practices making HAF success more or less likely. Analyzing these historical incidents, we suggested remediation approaches. The project also examined the legal concept of culpability (i.e., when intervention should have happened but didn’t) and proposed a state-machine-based analytic simulation to identify when HAF interventions are plausible. The panel objective will be to briefly present these concepts, but more generally to discuss designing for inevitable HAF events. This presentation with review the concept of a HAT Assistant to support multi-vehicle control of drones in a wildland firefighting context.