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.

Algorithmic recommender systems are a ubiquitous feature of contemporary cultural life online, suggesting music, movies, and other materials to their users. This article, drawing on fieldwork with developers of recommender systems in the US, describes a tendency among these systems’ makers to describe their purpose as ‘hooking’ people – enticing them into frequent or enduring usage. Inspired by steady references to capture in the field, the author considers recommender systems as traps, drawing on anthropological theories about animal trapping. The article charts the rise of ‘captivation metrics’ – measures of user retention – enabled by a set of transformations in recommenders’ epistemic, economic, and technical contexts. Traps prove useful for thinking about how such systems relate to broader infrastructural ecologies of knowledge and technology. As recommenders spread across online cultural infrastructures and become practically inescapable, thinking with traps offers an alternative to common ethical framings that oppose tropes of freedom and coercion.

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In their 'Critical Questions for Big Data', danah boyd and Kate Crawford warn: 'Taken out of context, Big Data loses its meaning'. In this short commentary, I contextualize this claim about context. The idea that context is crucial to meaning is shared across a wide range of disciplines, including the field of 'context-aware' recommender systems. These personalization systems attempt to take a user's context into account in order to make better, more useful, more meaningful recommendations. How are we to square boyd and Crawford's warning with the growth of big data applications that are centrally concerned with something they call 'context'? I suggest that the importance of context is uncontroversial; the controversy lies in determining what context is. Drawing on the work of cultural and linguistic anthropologists, I argue that context is constructed by the methods used to apprehend it. For the developers of 'context-aware' recommender systems, context is typically operationalized as a set of sensor readings associated with a user's activity. For critics like boyd and Crawford, context is that unquantified remainder that haunts mathematical models, making numbers that appear to be identical actually different from each other. These understandings of context seem to be incompatible, and their variability points to the importance of identifying and studying 'context cultures'-ways of producing context that vary in goals and techniques, but which agree that context is key to data's significance. To do otherwise would be to take these contextualizations out of context.

The world is talking ‘data’. The early cross‐disciplinary, business‐orientated hype around the potential of ‘big’ data, with its promises of unprecedented insight into social life, has given way. Data now motivates a sweep of dystopian visions, from rampant commodification to the invasion of privacy, political manipulation, and shadowy data doubles. Yet anthropologists have been cautious in taking data itself as their object, even as the social life of data practices becomes manifest in our ethnographies. In this introduction, we argue for an anthropology of data that is ethnographically specific and theoretically ambitious, putting forward a case for why anthropological engagements with the data moment might be not only politically important but also conceptually generative.

In recent years, many qualitative sociologists, anthropologists, and social theorists have critiqued the use of algorithms and other automated processes involved in data science on both epistemological and political grounds. Yet, it has proven difficult to bring these important insights into the practice of data science itself. We suggest that part of this problem has to do with under-examined or unacknowledged assumptions about the relationship between the two fields-ideas about how data science and its critics can and should relate. Inspired by recent work in Science and Technology Studies on interventions, we attempted to stage an encounter in which practicing data scientists were asked to analyze a corpus of critical social science literature about their work, using tools of textual analysis such as co-word and topic modelling. The idea was to provoke discussion both about the content of these texts and the possible limits of such analyses. In this commentary, we reflect on the planning stages of the experiment and how responses to the exercise, from both data scientists and qualitative social scientists, revealed some of the tensions and interactions between the normative positions of the different fields. We argue for further studies which can help us understand what these interdisciplinary tensions turn on-which do not paper over them but also do not take them as given.

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