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There is a growing literature on human-automation interaction in aviation, both real-world failures such as those described above and laboratory experiments (Wiener and Nagel, 1988; Sheridan (1992, 2002); Wickens et al, 1998; Decker and Hollnagel, 1999; Sarter and Amalberti, 2000; Sheridan and Parasuraman, 2006). It is clear that whatever the domain, the hardware and software are becoming more reliable with time and the problems point increasingly to the human interaction. Perrow (1984), for example, asserts that 60 to 80 percent of accidents are attributed to human error. It is not clear that the (probably unstoppable) trend toward further automation will change this.
By itself, automation (artificial sensors, computer logic, and mechanical actuators combined into control loops to perform given tasks) is not a bad thing. One can argue that it makes life better in numerous ways. The root problem lies in thinking that automation simply replaces people, and that since people are the ones who make errors, there will be fewer system failures when people “are removed from the system.” The fact is that people are not removed. Automating simply changes the role of the human user from that of direct, hands-on interaction with the vehicle, process, or device being controlled to that of a supervisor. A supervisor is required to plan the action, teach (program) the computer, monitor the action of the automation, intervene to replan and reprogram either if the automation fails or if it is insufficiently robust, and to learn from experience. (Sheridan, 1992, 2002)
Bainbridge (1987) was among the first to articulate what she called the “ironies of automation.” A first irony is that errors by the automation designers themselves make a significant contribution to human-automation failures. A second irony is that the same designer who seeks to eliminate human beings still relies on the human to perform the tasks the designer does not know how to automate.
In reference to the automation trend, Reason (1990) commented that “If a group of human factor specialists sat down with malign intent of conceiving an activity that was wholly ill-matched to the strengths and weaknesses of human cognition, they might well have come up with something not altogether different from what is currently demanded …”.
Which functions to allocate to humans and which functions to allocate to machines is an old question. Fitts (1951) published what has come to be called his MABA-MABA list:
Men (sic) are better at: detecting small amounts of visual, auditory or chemical energy; perceiving patterns of light or sound; improvising and using flexible procedures; storing information for long periods of time and recalling appropriate parts; reasoning inductively; and exercising judgment.
Machines are better at: responding quickly to control signals; applying great force smoothly and precisely; storing information briefly or erasing it completely; and reasoning deductively.
It is obvious that during the intervening half century some of Fitts’s assertions no longer ring fully true. Energy detection, pattern recognition, and information storage and retrieval have made considerable progress, though inductive reasoning and judgment remain elusive. Sheridan (2000) lists the following problems of function allocation:
In spite of our best efforts to cope with these and other problems of function allocation, error and dispute over allocation criteria are human nature. Perhaps that is part of the Darwinian reality, the requisite variety, the progenitor of progress. At least we have it in our power to say no to new technology, or do we?
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