Abstract
Data science is not simply a method but an organising idea. Commitment to the new paradigm overrides concerns caused by collateral damage, and only a counterculture can constitute an effective critique. Understanding data science requires an appreciation of what algorithms actually do; in particular, how machine learning learns. The resulting ‘insight through opacity’ drives the observable problems of algorithmic discrimination and the evasion of due process. But attempts to stem the tide have not grasped the nature of data science as both metaphysical and machinic. Data science strongly echoes the neoplatonism that informed the early science of Copernicus and Galileo. It appears to reveal a hidden mathematical order in the world that is superior to our direct experience. The new symmetry of these orderings is more compelling than the actual results. Data science does not only make possible a new way of knowing but acts directly on it; by converting predictions to pre-emptions, it becomes a machinic metaphysics. The people enrolled in this apparatus risk an abstraction of accountability and the production of ‘thoughtlessness’. Susceptibility to data science can be contested through critiques of science, especially standpoint theory, which opposes the ‘view from nowhere’ without abandoning the empirical methods. But a counterculture of data science must be material as well as discursive. Karen Barad’s idea of agential realism can reconfigure data science to produce both non-dualistic philosophy and participatory agency. An example of relevant praxis points to the real possibility of ‘machine learning for the people’.
Highlights
Data science is not a method but an organising idea
Typical routes into data science include someone with a background in statistics who learns to code, or someone strong in programming who has acquired an appreciation of analytical modelling and problem-solving
Where humans are part of the data science apparatus, what can be said about the effect on human agency of data science as an organising idea? By providing actionable numbers with the aura of authority, the algorithmic predictions become forceful at a human level
Summary
Data science is not a method but an organising idea. That is, an underlying shift in perspective and practices of the kind that Kuhn called a paradigm (Kuhn 1996). That is, it resonates with a belief in a hidden mathematical order that is ontologically superior to the one available to our everyday senses. It resonates with a belief in a hidden mathematical order that is ontologically superior to the one available to our everyday senses Looking at how this defines the character of data science provides a skeleton key to understanding its likely consequences. Data science does not affect by argument alone but acts directly in the world as a form of algorithmic force It is machinic, that is, an assembly of flows and logic that enrolls humans and technology in a larger, purposeful structure. As computation becomes pervasive, capturing and reorganising human activity, data science exerts its philosophy directly as orderings, decisions and outcomes
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