Doug Mitchell

“What epistemological violence is necessary to make the world readable to a machine learning system? AI seeks to systematize the unsystematizable, formalize the social, and convert an infinitely complex and changing universe into a Linnaean order of machine-readable tables. Many of AI’s achievements have depended on boiling things down to a terse set of formalisms based on proxies: identifying and naming some features while ignoring or obscuring countless others. To adapt a phrase from philosopher Babette Babich, machine learning exploits what it does know to predict what it does not know: a game of repeated approximations. Datasets are also proxies—stand-ins for what they claim to measure. Put simply, this is transmuting difference into computable sameness.”
― Kate Crawford, Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence