Machine Learning and the End of Theory: Reflections on a Data-Driven Conception of Health

Abstract

Taking the notion of health as a leitmotif, this paper discusses some conceptual boundaries for using machine learning⁠ - a data-driven, statistical, and computational technique in the field of artificial intelligence⁠ - for epistemic purposes and for generating knowledge about the world based solely on the statistical correlations found in data (i.e., the "End of Theory" view⁠).The thrust of the argument is that prior theoretical conceptions, subjectivity, and values would - because of their normative power⁠ - inevitably blight any effort at knowledge-making that seeks to be exclusively driven by data and nothing else. The conclusion suggests that machine learning will neither resolve nor mitigate⁠ the serious internal contradictions found in the "biostatistical theory" of health⁠ - the most well-discussed data-driven theory of health. The definition of notions such as these is an ongoing and fraught societal dialogue where the discussion is not only about what is, but also about what should be. This dialogical engagement is a question of ethics and politics ⁠and not one of mathematics.