Machine Learning and the Platformization of the Military: A Study of Google's Machine Learning Platform TensorFlow

Abstract

Abstract Against the background of the growing use of machine learning (ML) based technologies by the military, our article calls for an analytical perspective on ML platforms to understand how ML proliferates across the military and to what effects. Adopting a material–technical perspective on platforms as developed within new media studies, and bringing this literature to critical security studies, we suggest that a focus on platforms and the technical work they do is needed to understand how digital technologies are emerging and shaping security practices. Through a detailed study of Google's open-source ML platform TensorFlow and a discussion of the US Department of Defense Algorithmic Warfare Cross-Functional Team, or Project Maven, we make two broader contributions. First, we identify a broader “platformization” of the military, with which we refer to the growing involvement and permeation of the (technomaterial) ML platform as the infrastructure that enables new practices of decentralized and experimental algorithm development across the military. Second, we draw out how this platformization is accompanied by new entanglements between the military and actors in the corporate domain, especially Big Tech, which play a key role in this context, as well as the open-source community that is organized around these platforms.