The Displacement of Human Judgment in Science: The Problems of Biomedical Research in an Age of Big Data

in the human condition, hannah arendt lamented the state of the mathematical natural sciences. While recognizing their power to transform the world, she thought that they leave much to be desired in terms of providing an explanation for the world once they lose contact with human senses and intuitions. Surveying fields like quantum mechanics in which we still do not have a good picture of the world described by the equations, Arendt suggested that science was being reduced to mere technical intervention into nature rather than an understanding of it. "But the mathematization of physics … had in its last stages the unexpected and yet plausible consequence that every question man puts to nature is answered in terms of mathematical patterns to which no model can ever be adequate" (Arendt 1998, 287). Understanding is lost, leaving "us a universe of whose qualities we know no more than the way they affect our measuring instruments" (261). Of course, similar critiques were levelled throughout the mid-twentieth century by many other scholars—Edmund Husserl, Martin Heidegger, Hans Jonas, and Georges Canguilhem. As Husserl put it, "This arithmetization of geometry leads almost automatically, in a certain way, to the emptying of its meaning" (1970, 44). Physical science had lost hold of the original intuitions underlying the quantification of nature, driving it from the grasp of human understanding.

To many scholars who shared this critique, one natural science seemed like an exception: biology. Heidegger argued that "all sciences concerned with life must necessarily be inexact just in order to remain rigorous. A living thing can indeed also be grasped as a spatio-temporal magnitude of motion, but then it is no longer apprehended as living" (1977, 120). Because of the rich complexity of organisms, it was not thought that they could be understood purely mathematically. Instead, one needed to understand their purposes, aims, and goals; that is, their teleology. Even those like Ernst Mayr, attempting to mathematize biology as part of the Modern Synthesis of evolution, understood this need, although he preferred the cybernetically derived term "teleonomy" to the older "teleology" (Mayr 1989). For example, a complete, detailed description of the chemical and signaling reactions that lead to birds migrating would still be a deficient explanation if we did not also include an account of why birds migrate. Because it deals with living things, biology requires richer forms of judgment and explanation than other natural sciences.

This exceptional feature of biological explanation had been recognized already by Immanuel Kant, who argued that science needed something beyond the mathematics of Newtonian physics. While he would have liked to submit all of nature to that mathematical form of description, it was impossible because living organisms demanded this richer teleological description (Kant 2000, 386–88). Most philosophers in prior generations shared Kant's view. A science of purely mathematical relations between entities could not capture the complexity and goal-directedness of biological life.

Then, two decades ago, the Human Genome Project brought a sea change. Biology began to be inundated by massive amounts of genetic and other data, much of which was produced and interpreted by machines. Analogous to developments in other fields like medicine and education, human judgment began to look inadequate to the task of analyzing this data and using it to develop the therapies that had been promised in exchange for the massive public and private investment in the machine infrastructure. Scientists began suggesting that these vast accumulations of data, "mined" and interpreted by machines, could reveal knowledge. For instance, an editorial in Nature entitled "Can Biological Phenomena Be Understood by Humans?" quoted a biologist arguing that scientists "have to free [themselves] from the hypothesis-driven approach" and rely more on computer models (Anonymous 2000). After 2000, practicing scientists and philosophers of biology joined a broad debate over the relationship between the scientist, computer-analyzed data, and human reason in biological knowledge production (Allen 2001a, b; Gillies 2001; Smal-heiser 2002; Kell and Oliver 2004).

The argument for the seeming promise of artificial intelligence in science was put...