The stock phrase that “those who do not learn history are doomed to repeat it” certainly seems to hold true for technological innovation. After a team of Stanford University researchers recently developed an algorithm that they say is better at diagnosing heart arrhythmias than a human expert, all the MIT Technology Review could muster was to rhetorically ask if patients and doctors could ever put their trust in an algorithm. I won’t dispute the potential for machine learning algorithms to improve diagnoses; however, I think we should all take issue when journalists like Will Knight depict these technologies so uncritically, as if their claimed merits will be unproblematically realized without negative consequences.
Indeed, the same gee-whiz reporting likely happened during the advent of computerized autopilot in the 1970s—probably with the same lame rhetorical question: “Will passengers ever trust a computer to land a plane?” Of course, we now know that the implementation of autopilot was anything but a simple story of improved safety and performance. As both Robert Pool and Nicholas Carr have demonstrated, the automation of facets of piloting created new forms of accidents produced by unanticipated problems with sensors and electronics as well as the eventual deskilling of human pilots. That shallow, ignorant reporting for similar automation technologies, including not just automated diagnosis but also technologies like driverless cars, continues despite the knowledge of those previous mistakes is truly disheartening.
The fact that the tendency to not dig too deeply into the potential undesirable consequences of automation technologies is so widespread is telling. It suggests that something must be acting as a barrier to people’s ability to think clearly about such technologies. The political scientists Charles Lindblom called these barriers “impairments to critical probing,” noting the role of schools and the media in helping to ensure that most citizens refrain from critically examining the status quo.
Such impairments to critical probing with respect to automation technologies are visible in the myriad simplistic narratives that are often presumed rather than demonstrated, such as in the belief that algorithms are inherently safer than human operators. Indeed, one comment on Will Knight’s article prophesized that “in the far future human doctors will be viewed as dangerous compared to AI.”
Not only are such predictions impossible to justify—at this point they cannot be anything more than wildly speculative conjectures—but they fundamentally misunderstand what technology is. Too often people act as if technologies were autonomous forces in the world, not only in the sense that people act as if technological changes were foreordained and unstoppable but also in how they fail to see that no technology functions without the involvement of human hands. Indeed, technologies are better thought of as sociotechnical systems.
Even a simple tool like a hammer cannot existing without underlying human organizations, which provide the conditions for its production, nor can it act in the world without it having been designed to be compatible with the shape and capacities of the human body. A hammer that is too big to be effectively wielded by a person would be correctly recognized as an ill-conceived technology; few would fault a manual laborer forced to use such a hammer for any undesirable outcomes of its use.
Yet somehow most people fail to extend the same recognition to more complex undertakings like flying a plane or managing a nuclear reactor: in such cases, the fault is regularly attributed to “human error.” How could it be fair to blame a pilot, who only becomes deskilled as a result of their job requiring him or her to almost exclusively rely on autopilot, for mistakenly pulling up on the controls and stalling the plane during an unexpected autopilot error? The tendency to do so is a result of not recognizing autopilot technology as a sociotechnical system. Autopilot technology that leads to deskilled pilots, and hence accidents, is as poorly designed as a hammer incompatibly large for the human body: it fails to respect the complexities of the human-technology interface.
Many people, including many of my students, find that chain of reasoning difficult to accept, even though they struggle to locate any fault with it. They struggle under the weight of the impairing narrative that leads them to assume that the substitution of human action with computerized algorithms is always unalloyed progress. My students’ discomfort is only further provoked when presented with evidence that early automated textile technologies produced substandard, shoddy products—most likely being implemented in order to undermine organized labor rather than to contribute to a broader, more humanistic notion of progress. In any case, the continued power of automation=progress narrative will likely stifle the development of intelligent debate about automated diagnosis technologies.
If technological societies currently poised to begin automating medical care are to avoid repeating history, they will need to learn from past mistakes. In particular, how could AI be implemented so as to enhance the diagnostic ability of doctors rather than deskill them? Such an approach would part ways with traditional ideas about how computers should influence the work process, aiming to empower and “informate” skilled workers rather than replace them. As Siddhartha Mukherjee has noted, while algorithms can be very good at partitioning, e.g., distinguishing minute differences between pieces of information, they cannot deduce “why,” they cannot build a case for a diagnosis by themselves, and they cannot be curious. We only replace humans with algorithms at the cost of these qualities.
Citizens of technological societies should demand that AI diagnostic systems are used to aid the ongoing learning of doctors, helping them to solidify hunches and not overlook possible alternative diagnoses or pieces of evidence. Meeting such demands, however, may require that still other impairing narratives be challenged, particularly the belief that societies must acquiescence to the “disruptions” of new innovations, as they are imagined and desired by Silicon Valley elites—or the tendency to think of the qualities of the work process last, if at all, in all the excitement over extending the reach of robotics.
Taylor C. Dotson is an associate professor at New Mexico Tech, a Science and Technology Studies scholar, and a research consultant with WHOA. He is the author of The Divide: How Fanatical Certitude is Destroying Democracy and Technically Together: Reconstructing Community in a Networked World. Here he posts his thoughts on issues mostly tangential to his current research.
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