Introduction to thematic section on ‘social theory in an age of machine learning’

The proliferation of machine learning (ML) systems, which are algorithmic assemblages designed to extract patterns from data and make predictions, is visibly transforming society and everyday life. OpenAI's GPT-4 represents the latest development in this field, but even less remarkable ML systems have made significant inroads into important societal domains over the past decades. For instance, scholars have explored the deployment of ML systems in areas such as credit scoring (Kiviat 2019; Rona-Tas 2020), insurance (Cevolini and Esposito 2020), criminal justice (Brayne and Christin 2021), self-driving cars (Bissell et al. 2020; Stilgoe 2018), social media (Fourcade and Johns 2020), warfare (Scharre 2018), and automated trading (Hansen 2020; Hansen and Borch 2021).

A substantial and growing body of literature has examined the societal effects of these systems. Concerns have been raised about their potential biases (Zou and Schiebinger 2018), their contribution to racial and social inequalities (Benjamin 2019; Eubanks 2018; Noble 2018), and their transformative impact on subjectivity, everyday life, and labour markets (Shestakofsky 2017; Wajcman 2019). Scholars have also discussed the opacity of ML systems and the broader epistemological, ethical, and political implications they entail. These discussions have touched on established notions of accountability, expertise, liability, and more (Amoore 2020; Brighenti and Pavoni 2021; Burrell 2016; Coeckelbergh 2020; Collins 2018; Fazi 2020; Pasquale 2020; Svetlova 2021).

Simultaneously, there is a growing recognition, partially fuelled by these studies, that the rise of ML may have profound implications for social theory. On one hand, ML's use as a new methodological tool holds the promise of uncovering patterns in data that could prompt a reevaluation of established concepts used to describe the social world. While this promise may not yet be fully realized, some scholars are optimistic about ML's potential to generate theories by extracting non-linear patterns in data (Edelmann et al. 2020; Evans and Aceves 2016). On the other hand, the functioning of ML systems necessitates a reconceptualization of human-centered social theory (Airoldi 2022; Borch 2023; Esposito 2017; Yolgörmez 2021). In certain domains, the actionable predictions of ML systems not only inform human decision-making but replace it entirely (Borch and Min 2023). This distinction sets them apart from previous algorithmic systems and raises questions about accountability, control, ethics, liability, and politics. Additionally, it prompts inquiries into the ways in which their use may reshape human-machine configurations and potentially replace social relationships (Borch 2022).

This thematic section comprises three papers that contribute to the rethinking of social theory in light of ML, fitting into the broader context of the special issue on Political arithmetic: old and new. Each paper draws on different theoretical resources and approaches the connection between social theory and ML in distinct ways. They either reconsider established notions to comprehend ML or analyze the implications of ML for specific theoretical traditions.

In the paper titled ‘Hyperproduction: a social theory of deep generative models’, Fabian Ferrari and Fenwick McKelvey focus on synthetic media or deep generative models, such as Chat GPT and DALL-E, which create text, images, videos, and more. They argue that these models, closely tied to video game engines, should be understood as a form of hyperproduction – a circular dynamic blurring the boundaries between input and output. Moreover, they suggest that this hyperproduction is rooted in rentier capitalism, where the owners of data infrastructure extract rents for its use.

Richard Groß and Susann Wagenknecht take a different approach in their paper ‘Situating machine learning: On the calibration of problems in practice’. They centre their analysis on pragmatist theorization, drawing on Dewey's concept of the situation to explore ML as a practice. The paper examines the situated problems arising from the deployment of ML in art and science, including in relation to the training of models.

Dalia Mukhtar-Landgren and Alexander Paulsson, in their paper ‘From senses to sensors: autonomous cars and probing what machine learning does to mobilities studies’, investigate the use of ML-powered sensors in autonomous vehicles. They focus on how these vehicles aim to replace human senses and navigate roads. One crucial issue they raise is the role of autonomy and its significance within mobilities studies, where questions about autonomy assume a new form in the ML era, transforming human drivers into mere passengers.

Needless to say, the potential connections between ML and social theory extend beyond the papers featured in this thematic section. However, we hope that future research exploring the social theory implications of ML will build upon the foundation laid by these papers, leveraging the questions they raise and the propositions they put forth.

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No potential conflict of interest was reported by the author(s).

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Notes on contributors

Christian Borch

Christian Borch is Professor of Sociology at the Department of Sociology, University of Copenhagen. His recent work focuses on automated securities trading and its implications for social theory. His latest book is Social Avalanche: Crowds, Cities and Financial Markets (Cambridge University Press, 2020). He is currently working on a book titled Making Autonomous Markets: How Machine Learning Is Reshaping Financial Markets (under contract with Stanford University Press) and is co–editor, with Juan Pablo Pardo–Guerra, of the Oxford Handbook of the Sociology of Machine Learning (forthcoming with Oxford University Press).