I propose a novel explanation for why machine learning became a total social phenomenon. My emerging argument focuses on the interaction between the commodification of machine learning and a discourse favorable to automation. Technology corporations and consultants have made machine learning easier to buy and more interchangeable through cloud computing and thanks to easy-to-use software. Organizational decision-makers desire machine learning because they draw on a discourse favorable to automation. They use this discourse to frame organizational problems, imagine an automated future, and narrate a path from present problems to the future.
The taken-for-granted explanation for why machine learning is so pervasive is that “it works.” But that is not so clear. Consultants selling machine learning acknowledge that measuring the return on investment of machine learning is not easy. In 1996, a national partner of what is now PricewaterhouseCoopers encouraged corporations to “take on faith” that projects “will affect the bottom line.” Similarly, in 2018 two Deloitte-affiliated consultants argued that “[a]lthough the early successes are relatively modest, we anticipate that these technologies will eventually transform work.” Empirically, this evidence forces us to consider a new explanation. Theoretically, we may want to place instrumental rationality in a temporal context.
Emilio Lehoucq is a Ph.D. candidate in sociology at Northwestern University. He is interested in science and technology, social movements, culture, law, religion, statistical learning, and comparative-historical methods. His dissertation examines why machine learning became pervasive across the private and public sectors since the mid 1990s. His work has been published in Law & Social Inquiry and Mobilization.
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