|We rely on computers to fly our planes, find our cancers, design|
our buildings, audit our businesses. That's all well and good. But
what happens when the computer fails?
The crash, which killed all 49 people on board as well as one person on the ground, should never have happened. A National Transportation Safety Board investigation concluded that the cause of the accident was pilot error. The captain’s response to the stall warning, the investigators reported, “should have been automatic, but his improper flight control inputs were inconsistent with his training” and instead revealed “startle and confusion.” An executive from the company that operated the flight, the regional carrier Colgan Air, admitted that the pilots seemed to lack “situational awareness” as the emergency unfolded.
The Buffalo crash was not an isolated incident. An eerily similar disaster, with far more casualties, occurred a few months later. On the night of May 31, an Air France Airbus A330 took off from Rio de Janeiro, bound for Paris. The jumbo jet ran into a storm over the Atlantic about three hours after takeoff. Its air-speed sensors, coated with ice, began giving faulty readings, causing the autopilot to disengage. Bewildered, the pilot flying the plane, Pierre-Cédric Bonin, yanked back on the stick. The plane rose and a stall warning sounded, but he continued to pull back heedlessly. As the plane climbed sharply, it lost velocity. The airspeed sensors began working again, providing the crew with accurate numbers. Yet Bonin continued to slow the plane. The jet stalled and began to fall. If he had simply let go of the control, the A330 would likely have righted itself. But he didn’t. The plane dropped 35,000 feet in three minutes before hitting the ocean. All 228 passengers and crew members died.
Pilots today work inside what they call “glass cockpits.” The old analog dials and gauges are mostly gone.
They’ve been replaced by banks of digital displays. Automation has become so sophisticated that on a typical passenger flight, a human pilot holds the controls for a grand total of just three minutes. What pilots spend a lot of time doing is monitoring screens and keying in data. They’ve become, it’s not much of an exaggeration to say, computer operators.
And that, many aviation and automation experts have concluded, is a problem. Overuse of automation erodes pilots’ expertise and dulls their reflexes, leading to what Jan Noyes, an ergonomics expert at Britain’s University of Bristol, terms “a de-skilling of the crew.” No one doubts that autopilot has contributed to improvements in flight safety over the years. It reduces pilot fatigue and provides advance warnings of problems, and it can keep a plane airborne should the crew become disabled. But the steady overall decline in plane crashes masks the recent arrival of “a spectacularly new type of accident,” says Raja Parasuraman, a psychology professor at George Mason University and a leading authority on automation. When an autopilot system fails, too many pilots, thrust abruptly into what has become a rare role, make mistakes. Rory Kay, a veteran United captain who has served as the top safety official of the Air Line Pilots Association, put the problem bluntly in a 2011 interview with the Associated Press: “We’re forgetting how to fly.” The Federal Aviation Administration has become so concerned that in January it issued a “safety alert” to airlines, urging them to get their pilots to do more manual flying. An overreliance on automation, the agency warned, could put planes and passengers at risk.
Psychologists have found that when we work with computers, we often fall victim to two cognitive ailments—complacency and bias—that can undercut our performance and lead to mistakes. Automation complacency occurs when a computer lulls us into a false sense of security. Confident that the machine will work flawlessly and handle any problem that crops up, we allow our attention to drift. We become disengaged from our work, and our awareness of what’s going on around us fades. Automation bias occurs when we place too much faith in the accuracy of the information coming through our monitors. Our trust in the software becomes so strong that we ignore or discount other information sources, including our own eyes and ears. When a computer provides incorrect or insufficient data, we remain oblivious to the error.
What’s most astonishing, and unsettling, about computer automation is that it’s still in its early stages. Experts used to assume that there were limits to the ability of programmers to automate complicated tasks, particularly those involving sensory perception, pattern recognition, and conceptual knowledge. They pointed to the example of driving a car, which requires not only the instantaneous interpretation of a welter of visual signals but also the ability to adapt seamlessly to unanticipated situations. “Executing a left turn across oncoming traffic,” two prominent economists wrote in 2004, “involves so many factors that it is hard to imagine the set of rules that can replicate a driver’s behavior.” Just six years later, in October 2010, Google announced that it had built a fleet of seven “self-driving cars,” which had already logged more than 140,000 miles on roads in California and Nevada.
Driverless cars provide a preview of how robots will be able to navigate and perform work in the physical world, taking over activities requiring environmental awareness, coordinated motion, and fluid decision making. Equally rapid progress is being made in automating cerebral tasks. Just a few years ago, the idea of a computer competing on a game show like Jeopardy would have seemed laughable, but in a celebrated match in 2011, the IBM supercomputer Watson trounced Jeopardy’s all-time champion, Ken Jennings. Watson doesn’t think the way people think; it has no understanding of what it’s doing or saying. Its advantage lies in the extraordinary speed of modern computer processors.
In Race Against the Machine, a 2011 e-book on the economic implications of computerization, the MIT researchers Erik Brynjolfsson and Andrew McAfee argue that Google’s driverless car and IBM’s Watson are examples of a new wave of automation that, drawing on the “exponential growth” in computer power, will change the nature of work in virtually every job and profession. Today, they write, “computers improve so quickly that their capabilities pass from the realm of science fiction into the everyday world not over the course of a human lifetime, or even within the span of a professional’s career, but instead in just a few years.”
In a classic 1983 article in the journal Automatica, Lisanne Bainbridge, an engineering psychologist at University College London, described a conundrum of computer automation. Because many system designers assume that human operators are “unreliable and inefficient,” at least when compared with a computer, they strive to give the operators as small a role as possible. People end up functioning as mere monitors, passive watchers of screens. That’s a job that humans, with our notoriously wandering minds, are especially bad at. Research on vigilance, dating back to studies of radar operators during World War II, shows that people have trouble maintaining their attention on a stable display of information for more than half an hour. “This means,” Bainbridge observed, “that it is humanly impossible to carry out the basic function of monitoring for unlikely abnormalities.” And because a person’s skills “deteriorate when they are not used,” even an experienced operator will eventually begin to act like an inexperienced one if restricted to just watching. The lack of awareness and the degradation of know-how raise the odds that when something goes wrong, the operator will react ineptly. The assumption that the human will be the weakest link in the system becomes self-fulfilling.
Psychologists have discovered some simple ways to temper automation’s ill effects. You can program software to shift control back to human operators at frequent but irregular intervals; knowing that they may need to take command at any moment keeps people engaged, promoting situational awareness and learning. You can put limits on the scope of automation, making sure that people working with computers perform challenging tasks rather than merely observing. Giving people more to do helps sustain the generation effect. You can incorporate educational routines into software, requiring users to repeat difficult manual and mental tasks that encourage memory formation and skill building.
Some software writers take such suggestions to heart. In schools, the best instructional programs help students master a subject by encouraging attentiveness, demanding hard work, and reinforcing learned skills through repetition. Their design reflects the latest discoveries about how our brains store memories and weave them into conceptual knowledge and practical know-how. But most software applications don’t foster learning and engagement. In fact, they have the opposite effect. That’s because taking the steps necessary to promote the development and maintenance of expertise almost always entails a sacrifice of speed and productivity. Learning requires inefficiency. Businesses, which seek to maximize productivity and profit, would rarely accept such a trade-off. Individuals, too, almost always seek efficiency and convenience. We pick the program that lightens our load, not the one that makes us work harder and longer. Abstract concerns about the fate of human talent can’t compete with the allure of saving time and money.
The small island of Igloolik, off the coast of the Melville Peninsula in the Nunavut territory of northern Canada, is a bewildering place in the winter. The average temperature hovers at about 20 degrees below zero, thick sheets of sea ice cover the surrounding waters, and the sun is rarely seen. Despite the brutal conditions, Inuit hunters have for some 4,000 years ventured out from their homes on the island and traveled across miles of ice and tundra to search for game. The hunters’ ability to navigate vast stretches of the barren Arctic terrain, where landmarks are few, snow formations are in constant flux, and trails disappear overnight, has amazed explorers and scientists for centuries. The Inuit’s extraordinary way-finding skills are born not of technological prowess—they long eschewed maps and compasses—but of a profound understanding of winds, snowdrift patterns, animal behavior, stars, and tides.
Inuit culture is changing now. The Igloolik hunters have begun to rely on computer-generated maps to get around. Adoption of GPS technology has been particularly strong among younger Inuit, and it’s not hard to understand why. The ease and convenience of automated navigation makes the traditional Inuit techniques seem archaic and cumbersome.
But as GPS devices have proliferated on Igloolik, reports of serious accidents during hunts have spread. A hunter who hasn't developed way-finding skills can easily become lost, particularly if his GPS receiver fails. The routes so meticulously plotted on satellite maps can also give hunters tunnel vision, leading them onto thin ice or into other hazards a skilled navigator would avoid. The anthropologist Claudio Aporta, of Carleton University in Ottawa, has been studying Inuit hunters for more than 15 years. He notes that while satellite navigation offers practical advantages, its adoption has already brought a deterioration in way-finding abilities and, more generally, a weakened feel for the land. An Inuit on a GPS-equipped snowmobile is not so different from a suburban commuter in a GPS-equipped SUV: as he devotes his attention to the instructions coming from the computer, he loses sight of his surroundings. He travels “blindfolded,” as Aporta puts it. A unique talent that has distinguished a people for centuries may evaporate in a generation.
Whether it’s a pilot on a flight deck, a doctor in an examination room, or an Inuit hunter on an ice floe, knowing demands doing. One of the most remarkable things about us is also one of the easiest to overlook: each time we collide with the real, we deepen our understanding of the world and become more fully a part of it. While we’re wrestling with a difficult task, we may be motivated by an anticipation of the ends of our labor, but it’s the work itself—the means—that makes us who we are. Computer automation severs the ends from the means. It makes getting what we want easier, but it distances us from the work of knowing. As we transform ourselves into creatures of the screen, we face an existential question: Does our essence still lie in what we know, or are we now content to be defined by what we want? If we don’t grapple with that question ourselves, our gadgets will be happy to answer it for us.