Algorithms as culture: Some tactics for the ethnography of algorithmic systems

Terminological anxiety

At a conference on the social study of algorithms in 2013, a senior scholar stepped up to the audience microphone: “With all this talk about algorithms,” he said, “I haven’t heard anybody talk about an actual algorithm. Bubble sort, anyone?” ¹ Over two days, speakers had covered algorithmic topics from Google’s autocomplete feature to credit scoring, but the questioner was right: they had not examined anything like bubble sort, a simple algorithm for ordering a list. Bubble sort, the questioner implied, was an actual algorithm; autocomplete was something else.

The conference, Governing Algorithms, hosted at New York University, was an early moment in the growing transdisciplinary field of critical algorithm studies—broadly speaking, the application of humanistic and social scientific approaches to algorithms. ² But already, one of the field’s central tensions was evident: Did these humanists and social scientists, taking on objects that had, until recently, been the domain of computer scientists, know what they were talking about?

As “algorithm” drifted out of computer science and into popular and critical academic discourse, it seemed to signify a renewed concern for technical specificity. Where “Big Data” was vague—originating in an overheated marketing discourse—algorithms were precise. They were the core stuff of computer science, definitionally straightforward and, for many humanists, as distilled a case of rationalizing, quantifying, procedural logics as it was possible to find (see, e.g., Totaro and Ninno, 2014). The work to be done was clear: apply classic critiques of rationality, quantification, and procedure to these new objects and hit “publish.”

Yet, just as critical scholars picked them up, algorithms seemed to break apart. They had become, in the words of Governing Algorithms’ organizers, “somewhat of a modern myth” (Barocas et al., 2013: 1), attributed with great significance and power, but with ill-defined properties. Where an algorithm like bubble sort was so simple it could be described in a sentence, ³ the charismatic mega-algorithms that had caught scholars’ attention—Google’s search, Facebook’s newsfeed, New York City’s predictive policing system, Netflix’s recommender, and so on—were more complicated and harder to specify. Although these systems were called “algorithms” by the public, academics, and even by their makers, the distance between them and their Computer Science 101 counterparts was unsettling. Maybe, in our inexpert enthusiasm, critical algorithm studies scholars had made a mistake. Maybe we don’t understand what algorithms are, after all.

I take terminological anxiety to be one of critical algorithm studies’ defining features. But this is not because, as disciplinary outsiders, we are technically inept. Rather, it is because terminological anxieties are first and foremost anxieties about the boundaries of disciplinary jurisdiction, and critical algorithm studies is, essentially, founded in a disciplinary transgression. The boundaries of expert communities are maintained by governing the circulation and proper usage of professional argot, demarcating those who have the right to speak from those who do not (Fuller, 1991; Gal and Irvine, 1995; Gieryn, 1983), and algorithms are no different. Our worry about what “algorithm” means has more to do with our positions vis-a-vis other groups of experts than it has to do with our ability to correctly match terms and referents.

Rather than offering a “correct” definition, this article advances an approach to algorithms informed by their empirical profusion and practical existence in the wild—always at the boundaries of diverse communities of practice. It looks to anthropology for inspiration, both because my own training is anthropological and because anthropology proves useful for thinking through encounters between disparate knowledge traditions. It outlines an ethnographic approach to algorithms because ethnographic methods are well suited to the concerns that tend to occupy critical scholars—particularly concerns about how formalisms relate to culture. Ethnography roots these concerns in empirical soil, resisting arguments that threaten to wash away ordinary experience in a flood of abstraction. Rather than entering the field with a definition in hand, I propose using fieldwork to discover what algorithms are, in practice. After exploring two competing visions of what an anthropology of algorithms might entail, I offer a set of practical tactics for the ethnography of algorithmic systems, derived from my own ethnographic experience.

Anthropology 1: Algorithms in culture

A straightforward solution to our definitional crisis would be to take some expert definition as decisive: let computer scientists define “algorithms” and then examine how those things interact with our own areas of expertise. Like many straightforward solutions, this one has complications. As Paul Dourish has noted, “the limits of the term algorithm are determined by social engagements rather than by technological or material constraints” (2016: 3). That is to say, different people, in different historical moments and social situations have defined algorithms, and their salient qualities, differently. A data scientist working at Facebook in 2017, a university mathematician working on a proof in 1940, and a doctor establishing treatment procedures in 1995 may all claim, correctly, to be working on “algorithms,” but this does not mean they are talking about the same thing. An uncritical reliance on experts takes their coherence for granted and runs the risk of obscuring a key interest of critical scholars: what happens at the edges of knowledge regimes.

Given this instability and diversity, Dourish advances an anthropological case for taking on a “proper” expert definition of algorithms: we should do it not because this definition offers “a foundational truth of the nature of algorithms as natural occurrences,” but because it is what engineers do (2016: 2). In anthropological parlance, “algorithm” is an emic term, an actor’s term, part of the linguistic architecture through which software engineers make sense of the world and their work. Moreover, taking on such a definition facilitates critical algorithm scholars’ political projects: “Critiques of algorithmic reasoning that set their own terms of reference for key terminology,” he writes, “are unlikely to hit home” (Dourish, 2016: 2). If we want to understand engineers and get them to listen to us, then we need to use terms as they do; it is a matter “of ethnographic responsibility and practical politics” (Dourish, 2016: 2).

But what exactly is the emic definition of “algorithm,” and where should we find it? Dourish’s argument hinges on a definition of group boundaries. He writes:

When technical people get together, the person who says, ‘‘I do algorithms’’ is making a different statement than the person who says, ‘‘I study software engineering’’ or the one who says, ‘‘I’m a data scientist” and the nature of these differences matters to any understanding of the relationship between data, algorithms, and society. (Dourish, 2016: 3)

This is, however, an empirical question: Do the people who “do” algorithms today actually treat them according to this “proper” definition? Ethnography often throws analytic frameworks into disarray, and this proved true in my own fieldwork with US-based developers of algorithmic music recommender systems. After setting out to study engineers specifically, I realized that many more actors shaped the systems these companies built. Eventually, I interviewed half the employees of a recommender company that at the time employed roughly 80 people. These people’s jobs ranged from summer intern to CEO, and from systems engineer to front-end web developer. All of them, whether “technical people” or not, were party to the production of algorithms, but in the office, the “algorithm” seemed to be nowhere in particular.

So I sought it out, asking people to identify the algorithms they worked on. They typically balked at this question, and even people in the most “algorithmic” roles at the company, working on machine learning infrastructure or playlist personalization, located “the algorithm” just outside the scope of their work, somewhere in the company's code. One, a senior software engineer with a prestigious undergraduate degree in computer science told me that her training on algorithms in theory was irrelevant to her work on algorithms in practice, because algorithms in practice were harder to precisely locate: “It's very much black magic that goes on in there; even if you code a lot of it up, a lot of that stuff is lost on you.” The “algorithm” here was a collective product, and consequently everyone felt like an outsider to it.

When my interlocutors talked about algorithms, it was usually as part of a popular critical discourse that pitted algorithmic recommendation against human curators, claiming that “algorithms” could not understand music well enough to recommend it (e.g. Titlow, 2013). These engineers, humans within a system described as inhuman, resisted this framing: “algorithms are humans too,” one of my interlocutors put it, drawing the boundary of the algorithm around himself and his co-workers. Sitting in on ordinary practical work—whiteboarding sessions, group troubleshooting, and hackathon coding—I saw “algorithms” mutate, at times encompassing teams of people and their decisions, and at other times referring to emergent, surprisingly mysterious properties of the codebase. Only rarely did “algorithm” refer to a specific technical object like bubble sort. When pressed, many of my interlocutors could recite a proper definition, but these definitions were incidental to the everyday decision making I observed in the field. In practice, “algorithm” had a vague, “non-technical” meaning, indicating various properties of a broader “algorithmic system” (Seaver, 2013), even in nominally “technical” settings.

So, while Dourish’s argument for emic definition is sound, we cannot know those definitions in advance or assume that they will be precise and stable. If we look to the places where algorithms are made, we may not find a singular and correct sense of “algorithm.” Assuming that we will reifies a vision of the algorithm that risks obscuring humanistic concerns and blinding us to diversity in the field. “Technical people” are not the only people involved in producing the sociomaterial tangles we call “algorithms” and in practice, even they do not maintain the definitional hygiene that some critics have demanded of each other. A diverse crowd of people, using a wide array of techniques and understandings, produce the “algorithm” in a loosely coordinated confusion. Neglecting this is especially problematic for the algorithms that the public and most critics focus on: these are distributed, probabilistic, secret, continuously upgraded, and corporately produced.

Moreover, the “correct” definition of algorithms has been used precisely to isolate them from the concerns of social scientists and humanists, and it has been picked up by advocates and critics alike to set algorithmic processes apart from cultural ones. Dourish suggests that clarifying what algorithms are facilitates a discussion of how they interact with what they are not, and he provides a set of algorithmic “others” (things they are often confused with, but properly distinct from). This list makes clear how the proper definition of algorithms serves to distinguish them from typical critical concerns: algorithms are not automation (thus excluding questions of labor), they are not code (thus excluding questions of texts), they are not architecture (thus excluding questions of infrastructure), and they are not their materializations (thus excluding questions of sociomaterial situatedness) (2016: 3–5). Such a definition carves out a non-social abstract space for algorithms, artificially setting them apart from the various concerns that they tangle with in practice. The technologist who insists that his facial recognition algorithm has no embedded politics and the critic who argues that algorithmic music recommendation is an exogenous threat to culture both rely on an a priori distinction between cultural and technical stuff.

Let’s call this the algorithms in culture approach. It hinges on the idea that algorithms are discrete objects that may be located within cultural contexts or brought into conversation with cultural concerns. Understood as such, algorithms themselves are not culture. They may shape culture (by altering the flows of cultural material), and they may be shaped by culture (by embodying the biases of their creators), but this relationship is like that between a rock and the stream it is sitting in: the rock is not part of the stream, though the stream may jostle and erode it and the rock may produce ripples and eddies in the stream. In this view, algorithms can affect culture and culture can affect algorithms because they are distinct.

Anthropology 2: Algorithms as culture

Something like what has happened to computer scientists and the term “algorithm” happened earlier with anthropologists and “culture”: a term of art for the field drifted out of expert usage, and the experts lost control. Through the 1980s, American anthropologists were becoming generally skeptical of “culture” as an explanatory concept or object of study (Abu-Lughod, 1991). Its implicit holism and homogenizing, essentialist tendencies seemed politically problematic and ill-suited to the conflictual, changing shape of everyday life in anthropological field sites.

But while this skepticism grew, the culture concept gained purchase outside of anthropology: “cultures” were taken to mark diverse, bounded groups with timeless traditions, often synonymous with ethno-national identities; companies that once described their employees as a “family” might now say they had a “culture,” which designated an attitude toward work and, perhaps, what food and games were in the break room (Helmreich, 2001; Strathern, 1995). While anthropologists debated the usefulness or even existence of culture, it became a matter of concern among people with no obligation to anthropological definitions.

As anthropologists increasingly studied people with the power to resist outside explanations (Gusterson, 1996; Nader, 1969), this became a practical problem, not just an epistemological one. While anthropologists could critique the use of “culture” by fellow fieldworkers, these new users of “culture” were often influential parts of the social scene anthropologists wanted to describe. Groups of people linked by employer or ethnicity might take up “culture” as a project, making their understandings influential, even if anthropologists disagreed with them; vernacular theories of culture could shape social action in their image.

Consequently, many anthropologists turned from a vision of cultures as coherent symbolic orders to practice as the stuff of cultural life (Bourdieu, 1972; Ortner, 1984). As Lila Abu-Lughod put it, the practice approach to culture “is built around problems of contradiction, misunderstanding, and misrecognition, and favors strategies, interests, and improvisations over the more static and homogenizing cultural tropes of rules, models, and texts” (1991: 147). Rather than a setting for actions, culture might be something people do—an outcome of actions. This multiplies culture radically: instead of unified “cultures,” we have an array of loosely coordinated practices that compete and collaborate, sometimes for “cultural” goals and sometimes for other goals—for instance, “technical” ones.

Annemarie Mol has advanced a radical version of this focus on practice through her ethnographic and philosophical work, which she calls “praxiography” (2002). For Mol, reality itself is not prior to practices but rather a consequence of them; in Mol’s “practical ontology” (Gad et al., 2015), actors do not act on pre-given objects, but rather bring them into being—a process she calls “enactment.” Consequently, objects acted on in many different ways become “multiples”: “more than one and less than many” (Mol, 2002: 82). A “culture,” for instance, is not one coherent thing, nor is it a set of disparate things, such that every person enacts and imagines their own in isolation.

Following Laura Devendorf and Elizabeth Goodman, I find this a useful way to approach algorithms—not as stable objects interacted with from many perspectives, but as the manifold consequences of a variety of human practices. In their study of an online dating site (2014), Devendorf and Goodman found various actors enacted the site's algorithm differently: engineers tweaked their code to mediate between the distinctive behaviors of male and female users; some users tried to game the algorithm as they understood it, to generate more desirable matches; other users took the algorithm’s matches as oracular pronouncements, regardless of how they had been produced. No inner truth of the algorithm determined these interactions, and non-technical outsiders changed the algorithm's function: machine learning systems changed in response to user activity, and engineers accommodated user proclivities in their code.

We can call this the algorithms as culture position: algorithms are not technical rocks in a cultural stream, but are rather just more water. Like other aspects of culture, algorithms are enacted by practices which do not heed a strong distinction between technical and non-technical concerns, but rather blend them together. In this view, algorithms are not singular technical objects that enter into many different cultural interactions, but are rather unstable objects, culturally enacted by the practices people use to engage with them.

This vision of algorithms as culture differs from the notion of “algorithmic culture” (Striphas, 2015), which posits algorithms as an transformative force, exogenous to culture. In this view, a movie recommender is cultural because it shapes flows of cultural material, not because its algorithmic logics are themselves cultural (Hallinan and Striphas, 2016). Nor is it what Gillespie calls “algorithms becoming culture” (2016), which happens when algorithms become objects of popular debate and targets of strategic action (e.g. fans launching a listening campaign to influence a music recommender). Rather, algorithms are cultural not because they work on things like movies or music, or because they become objects of popular concern, but because they are composed of collective human practices. Algorithms are multiple, like culture, because they are culture.

Methodological enactments

If we understand algorithms as enacted by the practices used to engage with them, then the stakes of our own methods change. We are not remote observers, but rather active enactors, producing algorithms as particular kinds of objects through our research. Where a computer scientist might enact algorithms as abstract procedures through mathematical analysis, an anthropologist might use ethnographic methods to enact them as rangy sociotechnical systems constituted by human practices. A computer scientist may be concerned with matters of efficiency or how an algorithm interacts with data structures; an anthropologist may care instead about how an algorithm materializes values and cultural meanings. This disparity does not mean one of us is wrong and the other right—rather, we are engaged in different projects with different goals, and just as my discipline's methods are poorly suited to determining the efficiency of an algorithm in asymptotic time, so the computer scientist’s are poorly suited to understanding the cultural situations in which algorithms are built and implemented.

This recognition of algorithms’ multiplicity may seem destabilizing, causing trouble for efforts to hold algorithms accountable or even to identify their effects. However, I want to suggest that this approach facilitates, rather than limits, critique, making our accounts more adequate to the practices they describe, and centering those practices as a site of dispute and potential regulation. As Marina Welker puts it in Enacting the Corporation: “How we construct corporations as actors has crucial entailments for how we assign responsibilities to them, and vice versa” (2014: 4). We can replace “corporations” with “algorithms” in that sentence, but we do not have to: the algorithms we care about are very often corporate products, and they seem more similar to corporations themselves—in their heterogeneity, diffuseness, and legal architecture—than to textbook algorithms like the bubble sort that I began with. Presuming that algorithms must have a stable and coherent existence makes it harder, not easier, to grapple with their production and ongoing maintenance.

Algorithmic auditing provides a useful case for examining how critical methods enact algorithmic objects. These projects, inspired by historical efforts to expose housing discrimination, treat algorithmic systems as black-box functions: hidden operations that turn inputs into outputs, like mortgage applications into home loans (Diakopoulos, 2013; Sandvig et al., 2014). By varying inputs and examining the corresponding outputs (e.g. constructing personae that vary by apparent race), audit studies can demonstrate “disparate impact”—differences in outcome that affect legally protected classes of people and thus invoke regulatory response. What they cannot do is explain conclusively how that disparate impact came about.

By treating the “inside” of the algorithm as unknowable, these approaches participate in enacting an understanding of the algorithm as a black box, as knowable only through the relation between inputs and outputs. This is not to say that audit approaches are responsible for algorithmic secrecy—they are clearly responding to other efforts to keep algorithmic operations hidden—but they are part of a set of coordinated practices through which algorithms becomes understood as, and remain, secret.

While one might presume secrecy to be a simple matter of hiding facts that could be easily revealed, secrecy in practice is not so clear; secrecy is a social process, enacted by both insiders and outsiders (this is a longstanding trope in the anthropology of secrecy; Jones, 2014; Simmel, 1906). Different methods of engaging algorithmic secrecy enact the algorithm differently, setting its boundaries in different places—at the interface, at a certain point in the code, or at the legal boundary of the corporation. If, as Frank Pasquale (2015) suggests, we take algorithmic secrecy as a legal problem, then our efforts to understand the algorithm need to involve legal reasoning. If, as Jenna Burrell (2016) suggests, one source of algorithmic opacity is the intrinsic complexity of methods like neural networks, then we might try to engineer “explainable” systems (e.g. Aha, 2017). Thus, details of contract law may be as salient to the cultural functioning of algorithms as the ability of a neural network to “explain” its outputs.

As Daniel Neyland demonstrates through ethnographic work on an algorithmic accountability project, making algorithms accountable often means literally changing them—making them “account-able,” in ethnomedological jargon (2016). To make something account-able means giving it qualities that make it legible to groups of people in specific contexts. An accountable algorithm is thus literally different from an unaccountable one—transparency changes the practices that constitute it. For some critics this is precisely the point: the changes that transparency necessitates are changes that we want to have. This is a plain example of how different efforts to enact an object are both coordinated with each other and potentially in conflict. Transparency is not a revealing of what was always there, but a methodical reconfiguration of the social scene that changes it toward particular ends (see e.g. Ananny and Crawford, 2016; Strathern, 2000; Ziewitz, 2015).

Ethnographic tactics

This discussion recasts the anxiety described at the beginning of this article: concerns about the proper definition of “algorithm” are caught up not only in the boundary work that constitutes disciplines, but also in the methods those disciplines use. As algorithms are enacted by a wider range of methods, these enactments come into conflict with each other, and the work of coordinating among them becomes more challenging. Any method for apprehending algorithms now takes place amidst this confusion.

In this situation, I have found ethnography to be a useful method for enacting algorithms. Given its history in the study of cultural difference, it is well suited to life among plural methods—for engaging the various ways that people go about their lives, rather than trying to displace them. Not everyone needs to become an ethnographer, but ethnography as a method is distinctively appropriate to understanding how diverse methods interact. Ethnography is also good for seeing algorithms as, rather than in culture—for apprehending the everyday practices that constitute them and keep them working and changing, not just for observing how they relate to a distinct and properly “cultural” domain. Ethnographic methods help us gain purchase on topics that concern critical scholars: the interrelation of people and computers, the local production of abstract representations, and the human sensemaking that pervades algorithmic operations, locating these within sociocultural contexts that are diverse and changing.

In the remainder of this article, I offer a set of ethnographic tactics following the tradition of the “ethnography of infrastructure” (de Certeau, 1984; Star, 1999), which has directed researcher attention to the often-neglected cultural features of sociotechnical systems. These tactics are not unique to algorithmic objects—they have their origins in a variety of ethnographic domains. Nor should we expect every new object to engender brand new tactics; indeed, one appealing feature of seeing algorithms as, rather than in, culture is a rejection of the idea that they are new or unique at all. Where many methods for critically engaging algorithms take secrecy as one of algorithms’ defining features, I try here to offer paths away from secrecy, so that ethnographers might ask and answer other questions. Given traditional ethnographic concerns with access, this is a challenge, requiring openness to fieldwork that diverges from canonical imaginaries. I have found these tactics especially useful for making algorithms ethnographically tractable. They are a pragmatic counterpart to the conceptual argument advanced thus far—a set of theoretically informed “tricks of the trade” (Becker, 1998; Star, 1999).

Conclusion

I have argued for the merits of understanding algorithms as intrinsically cultural—as enacted by diverse practices, including those of “outside” researchers. Approaching algorithms ethnographically enacts them as part of culture, constituted not only by rational procedures, but by institutions, people, intersecting contexts, and the rough-and-ready sensemaking that obtains in ordinary cultural life. This enactment differs from canonical expert enactments, which hold algorithms to be essentially abstract procedures, and it differs from other cultural approaches to algorithms that try to locate them as forces on culture’s boundaries. Ethnography helps us to attend to empirical situations which are not necessarily stable or coherent.

Ethnography provides a useful orientation for entering and understanding worlds of meaning-laden practice, but conventional understandings of algorithms as defined by secret procedure suggest that ethnographic approaches are infeasible without a level of access that cannot realistically be obtained. The tactics I have laid out here are techniques for routing around that challenge; they work to enact algorithms not as inaccessible black boxes, but as heterogeneous and diffuse sociotechnical systems, with entanglements beyond the boundaries of proprietary software.

While I insist on imagining alternatives to visions of algorithms as essentially secret, this style of ethnographic enactment does not answer all the questions that people might want to ask of them. Questions about the particular workings of particular algorithms at particular moments in time remain broadly unanswerable so long as corporations are able to hide behind legal and technical secrecy. Nonetheless, a sense of the algorithm as multiple, and of ethnography as a practice for producing and participating in plural enactments of algorithms, offers fruitful avenues for producing actionable knowledge in spite of such secrecy.