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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. 42

42 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).

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