Joel M Haight is an Associate Professor of Industrial Engineering at the University of Pittsburgh, USA. He is currently a Professor and Director of the Safety
Engineering program, appointed in 2013. He has spent 4 years as a Research Manager with the U.S. Centers for Disease Control and Prevention (CDC), 10 years
as a Professor and Researcher at the Pennsylvania State University, Department of Energy and Mineral Engineering and nearly 18 years as an Engineer and
Manager at the Chevron Corporation


In today’s technologically driven world, we face the automation paradox. Bainbridge (1983) says that the more automated
a system becomes; the more important it is to consciously and appropriately integrate humans. We cannot argue that
automation provides immeasurable benefi t (improved effi ciency, reliability, accuracy, safety, etc.). However, if humans are not
actively engaged in system operation they experience loss of skill, knowledge, decision-making capability and reaction-time.
Without active engagement in the cognitive-based activities required by a control system, humans become less able to cope,
understand and function correctly. When called upon to take over control in an automated system, we become less capable of
eff ectively operating the system. Adaptive automation is a popular approach to solving this engagement problem. An adaptive
system allows the operator to have control until they are not engaged enough to know what to do in an upset. An NYC Amtrak
engineer derailed a train at 100 mph going into a 50 mph curve. Adaptive speed control gives the engineer the opportunity
to slow down; if no response, the control system takes over and slows the train. Researchers at the University of Pittsburgh
propose a design strategy integrating adaptive control while concurrently minimizing human error-induced incidents. Using
licensed nuclear power plant reactor operators in a control room simulator, researchers identify and measure key biometric
variables while varying task confi guration, automation level and override authority. If impact on system output is predictable,
the generated model can help designers simulate design strategies and resulting impact on system performance, thus providing
more-informed training protocols and content, more-informed simulator practice decisions and improved operational and
operating procedure consistency