Academic discussion on algorithmic vs. clinical prediction methods

ALGORISTS DREAM OF OBJECTIVITY Peter Galison Peter Galison is a science historian, Joseph Pellegrino University Professor and co- founder of the Black Hole Initiative at Harvard University, and the author of Einstein's Clocks and Poincaré’s Maps: Empires of Time. In his second-best book, the great medieval mathematician al-Khwarizmi described the new place-based Indian form of arithmetic. His name, soon sonically linked to “algorismus” (in late medieval Latin) came to designate procedures acting upon numbers—eventually wending its way through “algorithm,” (on the model of logarithm”), into French and on into English. But I like the idea of a modern algorist, even if my spellcheck does not. I mean by it someone profoundly suspicious of the intervention of human judgment, someone who takes that judgment to violate the fundamental norms of what it is to be objective (and therefore scientific). Near the end of the 20th century, a paper by two University of Minnesota psychologists summarized a vast literature that had long roiled the waters of prediction. One side, they judged, had for all too long held resolutely—and ultimately unethically— to the “clinical method” of prediction, which prized all that was subjective: “informal,” “in-the-head,” and “impressionistic.” These clinicians were people (so said the psychologists) who thought they could study their subjects with meticulous care, gather in committees, and make judgment-based predictions about criminal recidivism, college success, medical outcomes, and the like. The other side, the psychologists continued, embodied everything the clinicians did not, embracing the objective: “formal,” “mechanical,” “algorithmic.” This the authors took to stand at the root of the whole triumph of post-Galilean science. Not only did science benefit from the actuarial; to a great extent, science was the mechanical-actuarial. Breezing through 136 studies of predictions, across domains from sentencing to psychiatry, the authors showed that in 128 of them, predictions using actuarial tables, a multiple-regression equation, or an algorithmic judgment equalled or exceeded in accuracy those using the subjective approach. They went on to catalog seventeen fallacious justifications for clinging to the clinical. There were the self-interested foot-draggers who feared losing their jobs to machines. Others lacked the education to follow statistical arguments. One group mistrusted the formalization of mathematics; another excoriated what they took to be the actuarial “dehumanizing;” yet others said that the aim was to understand, not to predict. But whatever the motivations, the review concluded that it was downright immoral to withhold the power of the objective over the subjective, the algorithmic over expert judgment.” “? William M. Grove & Paul E. Meehl, “Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: The Clinical-Statistical Controversy,” Psychology, Public Policy, and Law, 2:2, 293-323 (1996). 160 HOUSE_OVERSIGHT_016963