Toward a political economy of synthetic data: A data-intensive capitalism that is not a surveillance capitalism?

Abstract

Surveillance of human subjects is how data-intensive companies obtain much of their data, yet surveillance increasingly meets with social and regulatory resistance. Data-intensive companies are thus seeking other ways to meet their data needs. This article explores one of these: the creation of synthetic data, or data produced artificially as an alternative to real-world data. I show that capital is already heavily invested in synthetic data. I argue that its appeal goes beyond circumventing surveillance to accord with a structural tendency within capitalism toward the autonomization of the circuit of capital. By severing data from human subjectivity, synthetic data contributes to the automation of the production of automation technologies like machine learning. A shift from surveillance to synthesis, I argue, has epistemological, ontological, and political economic consequences for a society increasingly structured around data-intensive capital.

Simulation and the Reality Gap: Moments in a prehistory of synthetic data

“…More specifically, the paper offers a contemporary analysis of synthetic data supported by a historical analysis of simulation. For the former, data was collected from synthetic data industry reports, company websites and interviews with workers and executives at six different synthetic data companies, echoing a labour process approach taken in previous work on synthetic data (Steinhoff, 2022) and AI production (Steinhoff, 2021). For the latter, archival research was conducted at two locations: the Ferranti Collection at the University of Manchester, UK, and the Brian W. Hollocks Collection (1961Collection ( -1982 held at NC State University Libraries, USA.…”

“…Social science research on synthetic data has only recently begun. Scholars have so far considered synthetic data in terms of privacy (Munkholm and Wiehn, forthcoming 2024), privacy regulation (Beduschi, 2024), the ethics of risk (Jacobsen, 2023;, law (Gal and Lynskey, 2023) and in terms of how it may contribute to a new mode of data-intensive, non-surveillance capitalism (Steinhoff, 2022). As an extension of critical work undertaken on big data, algorithms and AI over the past 10 years, these tentative examinations of synthetic data suggest that critiques of the sociotechnical dynamics of big data may become increasingly inadequate in their generality, as the big tech actors who control its research and development seek novel ways of extracting greater value from burgeoning computational assets, including rapidly evolving "rentier" strategies (Birch and Cochrane, 2021) in addition to well-established revenue streams.…”

“…Data synthesized in simulations comes labeled at a precise pixel level since all of the 3D elements of the simulation are discrete objects prior to rendering, reducing both labour time and costs (Nikolenko, 2021: v;Prakash et al, 2019) and thus avoiding, ostensibly, the need for surveillance (Steinhoff, 2022). This is, among other drivers, motivating research and investment in synthetic data.…”

On Digital Fetishism: A Critique of the Big Data Paradigm

“…Both the data uploaded by the users and those automatically extracted, this approach argues, require the participation of a human being 7 : either in terms of actively producing contents or exposing oneself to the tracking, spying, geo-localization, and to the quantification of one’s own self (see Till, 2014). In the other way, James Steinhoff writes, ‘the appeal of synthetic data for data-intensive capital’ is something well beyond the regime of ‘surveillance capitalism’ we have discussed in the previous section: it would even affirm ‘a historical tendency within capitalism toward the autonomization of the circuit of capital’ (Steinhoff, 2022: 2). A tendency toward the overcoming of people’s exploitation: it may well be, and here we will not take any position, as it is our belief that scientific research cannot foresee what is still to happen.…”

Close to the metal: Towards a material political economy of the epistemology of computation

“…However, as machine learning algorithms gained traction, they required and, in turn, propelled a turn towards datafication, data extraction and accumulation and, increasingly data production ostensibly ex nihilo through generative models for synthetic data (Jacobsen, 2023;Steinhoff, 2022). Propelled by the uptake of social media and portable devices and by broader turns across industries towards data-driven business models that made data monetization an essential component of the revenue and profitability of small and large businesses alike, datafication, 'surveillance capitalism' (Zuboff, 2019) and 'surveillance advertising' (Crain, 2021) turned towards the production of data as a commodity and as capital (Sadowski, 2019), that is, both as an asset that can be bought and sold, and as a mix of raw material and productive factor for the 'platform political economy' (Langley & Leyshon, 2021).…”