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Histories of Artificial Intelligence: A Genealogy of Power


I obtained my PhD in sociology from the School for Advanced Studies in the Social Sciences (EHESS) in Paris, and since 2009 have been a research fellow at the Higher School of Economics in Moscow. I am especially interested in the evolution of governmentalities during the second half of the XXth and the beginning of the XXIst centuries. Thus far, my research has been concerned with the intellectual and economic history of the Soviet Union and of the post-socialist 'transitions'. In particular, I've been looking at how economists and other intellectuals participate in fostering power discourses about managing economy and society in these contexts. My current research focuses on problems related to a growing role of AI specialists and data scientists in developing and promoting new tools of management in public policy as well as in corporate realms.

My current research project deals with the genealogies of machine learning, primarily as a technology for decision-making and control, in the capitalist West and in the socialist East. The Soviet case is interesting for several reasons. First, the field of ML has experienced exceptional development in this country: although very poorly presented in existing historiographies, Soviet contributions to ML are second in importance after the American achievements in this field. Second, the Soviet Union can be seen as a laboratory for designing and testing different ideas and technologies of ML from a control and decision-making perspective, due to the centralized nature of its political and economic regime. Finally, looking at the stories of ML in the USSR allows us to account for the variability of approaches and practices: it makes it possible to consider the development and uses of these technologies outside the neoliberal context and the digital economy with which they are currently associated. Such a historical and comparative perspective also makes it possible to question the existence of a universal 'algorithmic governmentality' and to study practical contexts in which these supposedly neutral technologies take their meaning. Besides this, in-depth discussion of the Soviet experiences could shed some light on the challenges of current developments and uses of these technologies. This project will also consider concrete examples of ML-based tools aiming at modeling collective choices and behaviors (economic simulations based on ML methods, urban mobility including autonomous or semi-autonomous actors, algorithmic management of labor-force). This analysis will allow us to consider the hypothesis that we are currently living in a world which increasingly resembles the last Soviet intellectual utopia of 'intelligent' control.

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