The Hostility to Algorithms

From the very outset, clinical psychologists responded to Meehl's ideas with hostility and disbelief. Clearly, they were in the grip of an illusion of skill in terms of their ability to make long-term predictions. On reflection, it is easy to see how the illusion came about and easy to sympathize with the clinicians' rejection of Meehl's research.

The statistical evidence of clinical inferiority contradicts clinicians' everyday experience of the quality of their judgments. Psychologists who work with patients have many hunches during each therapy session, anticipating how the patient will respond to an intervention, guessing what will happen next. Many of these hunches are confirmed, illustrating the reality of clinical skill.

The problem is that the correct judgments involve short-term predictions in the context of the therapeutic interview, a skill in which therapists may have years of practice. The tasks at which they fail typically require long-term predictions about the patient's future. These are much more difficult, even the best formulas do only modestly well, and they are also tasks that the clinicians have never had the opportunity to learn properly—they would have to wait years for feedback, instead of receiving the instantaneous feedback of the clinical session. However, the line between what clinicians can do well and what they cannot do at all well is not obvious, and certainly not obvious to them. They know they are skilled, but they don't necessarily know the boundaries of their skill. Not surprisingly, then, the idea that a mechanical combination of a few variables could outperform the subtle complexity of human judgment strikes experienced clinicians as obviously wrong.

The debate about the virtues of clinical and statistical prediction has always had a moral dimension. The statistical method, Meehl wrote, was criticized by experienced clinicians as "mechanical, atomistic, additive, cut and dried, artificial, unreal, arbitrary, incomplete, dead, pedantic, fractionated, trivial, forced, static, superficial, rigid, sterile, academic, pseudoscientific and blind." The clinical method, on the other hand, was lauded by its proponents as "dynamic, global, meaningful, holistic, subtle, sympathetic, con-figural, patterned, organized, rich, deep, genuine, sensitive, sophisticated, real, living, concrete, natural, true to life, and understanding."

This is an attitude we can all recognize. When a human competes with a machine, whether it is John Henry a-hammerin' on the mountain or the chess genius Garry Kasparov facing off against the computer Deep Blue, our sympathies lie with our fellow human. The aversion to algorithms making decisions that affect humans is rooted in the strong preference that many people have for the natural over the synthetic or artificial. Asked whether they would rather eat an organic or a commercially grown apple, most people prefer the "all natural" one. Even after being informed that the two apples taste the same, have identical nutritional value, and are equally healthful, a majority still prefer the organic fruit. Even the producers of beer have found that they can increase sales by putting "All Natural" or "No Preservatives" on the label.

The deep resistance to the demystification of expertise is illustrated by the reaction of the European wine community to Ashenfelter's formula for predicting the price of Bordeaux wines. Ashenfelter's formula answered a prayer: one might thus have expected that wine lovers everywhere would be grateful to him for demonstrably improving their ability to identify the wines that later would be good. Not so. The response in French wine circles, wrote The New York Times, ranged "somewhere between violent and hysterical." Ashenfelter reports that one oenophile called his findings "ludicrous and absurd." Another scoffed, "It is like judging movies without actually seeing them."

The prejudice against algorithms is magnified when the decisions are consequential. Meehl remarked, "I do not quite know how to alleviate the horror some clinicians seem to experience when they envisage a treatable case being denied treatment because a 'blind, mechanical' equation mis-classifies him." In contrast, Meehl and other proponents of algorithms have argued strongly that it is unethical to rely on intuitive judgments for important decisions if an algorithm is available that will make fewer mistakes. Their rational argument is compelling, but it runs against a stubborn psychological reality: for most people, the cause of a mistake matters. The story of a child dying because an algorithm made a mistake is more poignant than the story of the same tragedy occurring as a result of human error, and the difference in emotional intensity is readily translated into a moral preference.

Kahneman predicted that hostility would soften as algorithms became more familiar. He pointed to software recommendations for books and music, credit decisions, and statistical approaches to player selection or fourth-down decisions in sports. His hope was that experience with useful formulas would reduce the discomfort provoked by Meehl’s comparison of clinical and statistical prediction.