Adrian Mackenzie

About

Adrian Mackenzie is a leading scholar in science and technology studies (STS) and critical data studies, whose work interrogates the cultural and political implications of machine learning and algorithmic systems. His most-cited monograph, *Machine Learners* (2017, 226 citations), fundamentally reframes machine learning not merely as a technical tool but as a practice that reshapes knowledge production and critical thought itself. By examining how algorithms learn from data across domains—from medical research to autonomous vehicles—Mackenzie reveals the infrastructures, labor, and epistemic shifts embedded in these systems. His research consistently bridges computational methods with social theory, exploring how code, classification, and data practices enact power and transform everyday life. Beyond this landmark book, his broader portfolio includes influential studies on wireless networks, software, and the politics of technical standards. With a career dedicated to making the opaque workings of contemporary computation visible and contestable, Mackenzie has become a vital voice for understanding how machine learning reconfigures not just science and industry, but the very conditions of critique. His work remains essential reading for anyone seeking to critically engage with the algorithmic condition.

Research Focus

Key Achievements

Top Papers

1. 1
   Machine Learners

   226 citations · 2017

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