The aim of the article is to intervene in debates about the digital and, in particular, framings that imagine the digital in terms of epochal shifts or as redefining life. Instead, drawing on recent developments in digital methods, we explore the lively, productive and performative qualities of the digital by attending to the specificities of digital devices and how they interact, and sometimes compete, with older devices and their capacity to mobilize and materialize social and other relations. In doing so, (...) our aim is to explore the implications of digital devices and data for reassembling social science methods or what we call the social science apparatuses that assemble digital devices and data to ‘know’ the social and other relations. Building on recent work at CRESC on the social life of methods, we recommend a genealogical approach that is alive to the ways in which digital devices are simultaneously shaped by social worlds, and can in turn become agents that shape those worlds. This calls for attending to the specificities of digital devices themselves, how they are varied and composed of diverse socio-technical arrangements, and are enrolled in the creation of new knowledge spaces, institutions and actors. Rather than exploring what large-scale changes can be revealed and understood through the digital, we argue for explorations of how digital devices themselves are materially implicated in the production and performance of contemporary sociality. To that end we offer the following nine propositions about the implications of digital data and devices and argue that these demand rethinking the theoretical assumptions of social science methods: transactional actors; heterogeneity; visualization; continuous time; whole populations; granularity; expertise; mobile and mobilizing; and non-coherence. (shrink)

The aim of this article is to provide a discussion of scholarly ‘crisis of modernity’ discourses that have developed in the field of social philosophy. Re-visiting past and present discourses can be illuminating in at least three ways: it can reveal the broader picture of the present financialized and technologized world and the rise of financial technologies; it can provide scholars with new vocabularies, concepts, and metaphors to comprehend present-day phenomena and developments; and it can reveal the variety of commitments (...) that are possible, today, too. This article starts with a discussion of the original ‘crisis of modernity’ discourses, in which the clashing arguments of Comte and Tocqueville are featured, and a discussion of a second ‘crisis of modernity’ that developed in the context of the ‘Great Depression.’ A third ‘crisis of modernity’ discourse emerged in the wake of the financial crises of the 1970s. Such crises are still ongoing and discussed within the boundaries of the third ‘crisis of modernity’ discourse. How financial technologies do and do not fit within this third discourse is discussed in the remainder of this article. (shrink)

Machine learning has become a key component of contemporary information systems. Unlike prior information systems explicitly programmed in formal languages, ML systems infer rules from data. This paper shows what this difference means for the critical analysis of socio-technical systems based on machine learning. To provide a foundation for future critical analysis of machine learning-based systems, we engage with how the term is framed and constructed in self-education resources. For this, we analyze machine learning tutorials, an important information source for (...) self-learners and a key tool for the formation of the practices of the machine learning community. Our analysis identifies canonical examples of machine learning as well as important misconceptions and problematic framings. Our results show that machine learning is presented as being universally applicable and that the application of machine learning without special expertise is actively encouraged. Explanations of machine learning algorithms are missing or strongly limited. Meanwhile, the importance of data is vastly understated. This has implications for the manifestation of social inequalities through machine learning-based systems. (shrink)

The article discusses the use of algorithmic models in finance. Algo trading is widespread but also somewhat controversial in modern financial markets. It is a form of automated trading technology, which critics claim can, among other things, lead to market manipulation. Drawing on three cases, this article shows that manipulation also can happen in the reverse way, meaning that human traders attempt to make algorithms ‘make mistakes’ by ‘misleading’ them. These attempts to manipulate are very simple and immediately transparent to (...) humans. Nevertheless, financial regulators increasingly penalize such attempts to manipulate algos. The article explains this as an institutionalization of algo trading, a trading practice which is vulnerable enough to need regulatory protection. (shrink)