Abstract
This chapter argues for an engaged approach to the techno-politics of platforms, data, and algorithms, drawing on three empirical vignettes on music recommender systems. The three vignettes are neither about individual taste, nor about its colonialization by a metaphorical machinery of the cultural industry, but about how the logistics of music consumption has been transformed into data science and data engineering problems. Based on these vignettes the chapter will highlight three levels of techno-politics involved in the current upgrade of our TechnoScienceSocieties infrastructural nerves. On the level of platforms and infrastructures, these techno-politics reorganize a flexible infrastructure into integrated, but locked-up platforms; on the level of data they deal with rearranging existing assemblages of institutional and technical legacies; on the level of algorithms they are reorganizing our common world as computational processes. The chapter ends with a proposal for interventions and redesigns of such techno-politics of platforms, data and algorithms inside that array of projects, processes and practices of reassembling the infrastructural nerves of our TechnoScienceSocieties.
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Notes
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I use the term ‘techno-politics’ in the way that Timothy Mitchell has used it in his book on experts and Egypt to describe a “transformation at the material level as much as the level of representations” (Mitchel 2002). Mitchell does not really define his use of the term, but by opposing it to James C. Scott’s analysis of the uses and misuses of science and technology by modern nation states, he points out that his use of the term is to focus on the “social and political practice that produce simultaneously the powers of science and the powers of modern states” (Footnote 77: 43). I use the term in this way, although I use the term “politics” in a much broader sense that treats state-oriented politics as only one techno-political arrangement amongst others (see Passoth and Rowland 2010, 2015).
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In the strict terms of machine learning concepts, such a practice would not count as ‘teaching’ as this would require the careful collection of training data and (counter) examples to ‘learn’ the models in the first place.
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Passoth, JH. (2020). Music, Recommender Systems and the Techno-Politics of Platforms, Data, and Algorithms. In: Maasen, S., Dickel, S., Schneider, C. (eds) TechnoScienceSociety. Sociology of the Sciences Yearbook, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-43965-1_9
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