"Next to Sartre's Search for a Method and in direct opposition to it, Foucault's work is the most noteworthy effort at a theory of history in the last 50 years." -- Library Journal.

In the past twenty years, the field of science and technology studies (S&TS) has made considerable progress toward illuminating the relationship between scientific knowledge and political power. These insights have not yet been synthesized or presented in a form that systematically highlights the connections between S&TS and other social sciences. This timely collection of essays by some of the leading scholars in the field attempts to fill that gap. The book develops the theme of "co-production", showing how scientific knowledge both (...) embeds and is embedded in social identities, institutions, representations and discourses. Accordingly, the authors argue, ways of knowing the world are inseparably linked to the ways in which people seek to organize and control it. Through studies of emerging knowledges, research practices and political institutions, the authors demonstrate that the idiom of co-production importantly extends the vocabulary of the traditional social sciences, offering fresh analytic perspectiveson the nexus of science, power and culture. (shrink)

Marketing and web analytic companies have implemented sophisticated algorithms to observe, analyze, and identify users through large surveillance networks online. These computer algorithms have the capacity to infer categories of identity upon users based largely on their web-surfing habits. In this article I will first discuss the conceptual and theoretical work around code, outlining its use in an analysis of online categorization practices. The article will then approach the function of code at the level of the category, arguing that an (...) analysis of coded computer algorithms enables a supplement to Foucauldian thinking around biopolitics and biopower, of what I call soft biopower and soft biopolitics. These new conceptual devices allow us to better understand the workings of biopower at the level of the category, of using computer code, statistics and surveillance to construct categories within populations according to users’ surveilled internet history. Finally, the article will think through the nuanced ways that algorithmic inference works as a mode of control, of processes of identification that structure and regulate our lives online within the context of online marketing and algorithmic categorization. (shrink)

Today’s bodies are akin to ‘walking sensor platforms’. Bodies either host, or are the subjects of, an array of sensing devices that act to convert bodily movements, actions and dynamics into circulative data. This article proposes the notions of ‘disembodied exhaust’ and ‘embodied exhaustion’ to conceptualise processes of bodily sensorisation and datafication. As the material body interfaces with networked sensor technologies and sensing infrastructures, it emits disembodied exhaust: gaseous flows of personal information that establish a representational data-proxy. It is this (...) networked actant that progressively structures how embodied subjects experience their daily lives. The significance of this symbiont medium in determining the outcome of interplays between networked individuals and audiences necessitates that it is carefully contrived. The article explores the nature and function of the data-proxy, and its impact on social relations. Drawing on examples that depict individuals engaging with their data-proxies, the article suggests that managing a virtual presence is analogous to a work relation, demanding diligence and investment. But it also shows how the data-proxy operates as a mode of affect that challenges conventional distinctions made between organic and inorganic bodies, agency and actancy, mortality and immortality, presence and absence. (shrink)

This article responds to recent debates in critical algorithm studies about the significance of the term “algorithm.” Where some have suggested that critical scholars should align their use of the term with its common definition in professional computer science, I argue that we should instead approach algorithms as “multiples”—unstable objects that are enacted through the varied practices that people use to engage with them, including the practices of “outsider” researchers. This approach builds on the work of Laura Devendorf, Elizabeth Goodman, (...) and Annemarie Mol. Different ways of enacting algorithms foreground certain issues while occluding others: computer scientists enact algorithms as conceptual objects indifferent to implementation details, while calls for accountability enact algorithms as closed boxes to be opened. I propose that critical researchers might seek to enact algorithms ethnographically, seeing them as heterogeneous and diffuse sociotechnical systems, rather than rigidly constrained and procedural formulas. To do so, I suggest thinking of algorithms not “in” culture, as the event occasioning this essay was titled, but “as” culture: part of broad patterns of meaning and practice that can be engaged with empirically. I offer a set of practical tactics for the ethnographic enactment of algorithmic systems, which do not depend on pinning down a singular “algorithm” or achieving “access,” but which rather work from the partial and mobile position of an outsider. (shrink)

Critical Data Studies explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact with society, its organization and functioning, and the resulting impact on individuals’ daily lives. CDS questions the (...) many assumptions about Big Data that permeate contemporary literature on information and society by locating instances where Big Data may be naively taken to denote objective and transparent informational entities. In this introduction to the Big Data & Society CDS special theme, we briefly describe CDS work, its orientations, and principles. (shrink)

In this important new study Ian Hacking continues the enquiry into the origins and development of certain characteristic modes of contemporary thought undertaken in such previous works as his best selling Emergence of Probability. Professor Hacking shows how by the late nineteenth century it became possible to think of statistical patterns as explanatory in themselves, and to regard the world as not necessarily deterministic in character. Combining detailed scientific historical research with characteristic philosophic breath and verve, The Taming of Chance (...) brings out the relations among philosophy, the physical sciences, mathematics and the development of social institutions, and provides a unique and authoritative analysis of the "probabilization" of the Western world. (shrink)

In recent years, educational institutions have started using the tools of commercial data analytics in higher education. By gathering information about students as they navigate campus information systems, learning analytics “uses analytic techniques to help target instructional, curricular, and support resources” to examine student learning behaviors and change students’ learning environments. As a result, the information educators and educational institutions have at their disposal is no longer demarcated by course content and assessments, and old boundaries between information used for assessment (...) and information about how students live and work are blurring. Our goal in this paper is to provide a systematic discussion of the ways in which privacy and learning analytics conflict and to provide a framework for understanding those conflicts. -/- We argue that there are five crucial issues about student privacy that we must address in order to ensure that whatever the laudable goals and gains of learning analytics, they are commensurate with respecting students’ privacy and associated rights, including (but not limited to) autonomy interests. First, we argue that we must distinguish among different entities with respect to whom students have, or lack, privacy. Second, we argue that we need clear criteria for what information may justifiably be collected in the name of learning analytics. Third, we need to address whether purported consequences of learning analytics (e.g., better learning outcomes) are justified and what the distributions of those consequences are. Fourth, we argue that regardless of how robust the benefits of learning analytics turn out to be, students have important autonomy interests in how information about them is collected. Finally, we argue that it is an open question whether the goods that justify higher education are advanced by learning analytics, or whether collection of information actually runs counter to those goods. (shrink)