For the past two decades, fields of knowledge-production that utilize statistics have adopted machine learning as their primary mode of operation (Mackenzie 2013, 434). Due to the advances of computational technology, machines can now be programmed to find patterns in large datasets.

Machine learners ^[1] recursively use
patterns to infer correlations, essentially hailing new performative judgments on the world. Adrian Mackenzie goes so far as to claim that we now live within a regime of predictivity characterized by computational practices that rely less on verification than inference and abductive reasoning. With the widespread use of machine learning practices, abduction creates an overall "sensibility to change and alter events" (Mackenzie 2013, 402). By abstracting concrete social practices into data vectors, machine learners measure, forecast and thus modulate human behaviors by essentially scripting performatives. Put simply, machine learners have become some of the most potent social inscription devices today. It is within this context that my dissertation asks – how does the recent ubiquity of machine learning affect the production of subjectivity?

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[1]  The generalized practice of machine learning encompasses many techniques of predictive modeling that are used to classify events and things into stable categories. Some of these techniques include linear regression models, Bayesian classifiers, and k-nearest neighbors. Decision trees, deep belief networks, and neural networks will feature more prominently in my project. However, the research on machine learning is evolving, seemingly on a week to week basis.


[2] Background Image Credit- Kate Crawford and Vladan Joler.