Оглавление
Massimo Airoldi. Machine Habitus
Table of Contents
List of Illustrations
List of Tables
Guide
Pages
Machine Habitus. Toward a Sociology of Algorithms
Copyright Page
Quote
Acknowledgments
Figures and Tables. Figures
Tables
Preface
1 Why Not a Sociology of Algorithms? Machines as sociological objects
Algorithms and their applications, from Euclid to AlphaGo
Analogue Era (–1945)
Digital Era (1946–1998)
Platform Era (1998–)
Critical algorithm studies
Open questions and feedback loops
Seeing algorithms with the eyes of Pierre Bourdieu
Notes
2 Culture in the Code. Born and raised in Torpignattara
Humans behind machines. Machine creators
Machine trainers
Society in, society out
Data contexts. Traces and patterns
Global and local
Machine socialization. Practical reason and machine habitus
Primary and secondary machine socialization
Notes
3 Code in the Culture. The haircut appointment
Algorithms: agency and authority. Machine agency
Computational authority
Algorithmic distinctions
How socialized machines interact. More-than-human relations
Informational asymmetry and cultural alignment
A typology of user–machine interactions
Platforms as techno-social fields. Encapsulating and confounding
Reinforcing, or transforming?
4 A Theory of Machine Habitus. Premises
Structures. Social structure
Digital infrastructure
Entanglements
Trajectories. Temporality
Multiplicity
Boundaries. Social, symbolic and automated
Four scenarios of techno-social reproduction
5 Techno-Social Reproduction. Toward a sociology of algorithms as social agents
An old but new research agenda
Beyond a sociology of algorithms
Bibliography
Index
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Massimo Airoldi
Pierre Bourdieu
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A major part of this critical literature has scrutinized the production of the input of automated calculations, that is, the data. Critical research on the mining of data through digital forms of surveillance (Brayne 2017; van Dijck 2013) and labour (Casilli 2019; Gandini 2020) has illuminated the extractive and ‘panopticist’ character of platforms, Internet services and connected devices such as wearables and smartphones (see Lupton 2020; Ruckenstein and Granroth 2020; Arvidsson 2004). Cheney-Lippold (2011, 2017) developed the notion of ‘algorithmic identity’ in order to study the biopolitical implications of web analytics firms’ data harnessing, aimed at computationally predicting who digital consumers are. Similar studies have also been conducted in the field of critical marketing (Cluley and Brown 2015; Darmody and Zwick 2020; Zwick and Denegri-Knott 2009). Furthermore, a number of works have questioned the epistemological grounds of ‘big data’ approaches, highlighting how the automated and decontextualized analysis of large datasets may ultimately lead to inaccurate or biased results (boyd and Crawford 2012; O’Neil 2016; Broussard 2018). The proliferation of metrics and the ubiquity of ‘datafication’ – that is, the transformation of social action into online quantified data (Mayer-Schoenberger and Cukier 2013) – have been indicated as key features of today’s capitalism, which is seen as increasingly dependent on the harvesting and engineering of consumers’ lives and culture (Zuboff 2019; van Dijck, Poell and de Waal 2018).
As STS research did decades earlier with missiles and electric bulbs (MacKenzie and Wajcman 1999), critical algorithm studies have also explored how algorithmic models and their data infrastructures are developed, manufactured and narrated, eventually with the aim of making these opaque ‘black boxes’ accountable (Pasquale 2015). The ‘anatomy’ of AI systems is the subject of the original work of Crawford and Joler (2018), at the crossroads of art and research. Taking Amazon Echo – the consumer voice-enabled AI device featuring the popular interface Alexa – as an example, the authors show how even the most banal human–device interaction ‘requires a vast planetary network, fueled by the extraction of non-renewable materials, labor, and data’ (Crawford and Joler 2018: 2). Behind the capacity of Amazon Echo to hear, interpret and efficiently respond to users’ commands, there is not only a machine learning model in a constant process of optimization, but also a wide array of accumulated scientific knowledge, natural resources such as the lithium and cobalt used in batteries, and labour exploited in the mining of both rare metals and data. Several studies have looked more closely into the genesis of specific platforms and algorithmic systems, tracing their historical evolution and practical implementation while simultaneously unveiling the cultural and political assumptions inscribed in their technicalities (Rieder 2017; D. MacKenzie 2018; Helmond, Nieborg and van der Vlist 2019; Neyland 2019; Seaver 2019; Eubanks 2018; Hallinan and Striphas 2016; McKelvey 2018; Gillespie 2018). Furthermore, since algorithms are also cultural and discursive objects (Beer 2017; Seaver 2017; Bucher 2017; Campolo and Crawford 2020), researchers have investigated how they are marketed and – as often happens – mythicized (Natale and Ballatore 2020; Neyland 2019). This literature shows how the fictitious representation of calculative devices as necessarily neutral, objective and accurate in their predictions is ideologically rooted in the techno-chauvinistic belief that ‘tech is always the solution’ (Broussard 2018: 7).
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