Machine Habitus. Massimo Airoldi

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Machine Habitus - Massimo Airoldi

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kinds of agents – humans and machine learning systems – and their cultural ‘black boxes’ – habitus and machine habitus – jointly make society, while being made by it.

      The following chapters attempt to bridge insights from cultural sociology and computer science, AI research and Science and Technology Studies. Chapter 2 will help us understand the social genesis of the machine habitus, and how it must be distinguished from another type of culture in the code, present in any technological artefact; that is, the culture of its human creators, acting as a deus in machina.7 Chapter 3 will focus on the code in the culture, aiming to theorize the forms and effects of algorithmic practice, and how these concretize within the techno-social fields of digital platforms. Chapter 4 will then bring the culture in the code and code in the culture together in order to sketch a general theory of machine habitus in action. The concluding chapter will highlight the sociological relevance of mechanisms of techno-social reproduction and propose alternative ways to imagine, design and relate to socialized machines in real life.

      1  1 The status of artificial intelligence – whether it could ever become ‘general’, or is destined to remain a ‘narrow’ mathematical manifestation as in the case of current machine learning systems – is a debated issue that goes beyond the scope of the present work. For a non-technical overview, see Broussard 2018.

      2  2 For an overview of AI systems and deep learning methods, see Kelleher 2019. For an introduction to music recommenders, see Celma 2010. Historical accounts and non-technical explanations of machine learning systems can be found in Pasquinelli 2017; Broussard 2018; Sumpter 2018; Natale and Ballatore 2020; and Mackenzie 2015.

      3  3 Chen writes: ‘in 1983, CPU speed or frequency was 25 MHz. Assuming 1,000 CPU cycles correspond to making a decision on a Go board, it would have taken 31 minutes to make a decision about 361 × 360 × 359 = 46,655,640 possible moves back then. Today, a single CPU’s frequency is above 4 GHz, […] making it 160 times faster on the same algorithm – in this case, taking less than 12 seconds to make that same decision’ (2016: 6).

      4  4 Since 2015, special issues on the critical study of algorithms have appeared in recognized social science journals such as the European Journal of Cultural Studies, Information, Communication & Society, New Media & Society, Technology & Human Values, Theory, Culture & Society, and more.

      5  5 The concept of socialization has been at the root of sociological theory since its very beginnings, as a way to explain mechanisms of social reproduction. As Guhin, Calarco and Miller-Idriss (2020) note, the term has become increasingly contested in post-war American sociology. In fact, Parsons’ functionalist notion of socialization has been criticized for being a ‘downloading’ model of culture which downplays the centrality of social interaction. The concept was subsequently employed in different terms by authors such as Giddens, Foucault, Berger and Luckmann, Luhmann, and Bourdieu, and continues to bear theoretical relevance today.

      6  6 For an account of the genesis of the concept of habitus and of its pre-Bourdieusian uses by authors such as Elias, Mauss, Merleau-Ponty and Panofsky, see Sterne 2003 and Lizardo 2004.

      7  7 Deus in machina is a wordplay based on deus ex machina, or ‘god from the machine’ – originally indicating the mechanical systems used to bring onstage actors playing gods in the ancient Greek theatre. I must thank Mauro Barisione, who came up with it during our early email exchanges about this book project.

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