Algorithm [draft] [#digitalkeywords]

“What we are really concerned with when we invoke the “algorithmic” here is not the algorithm per se but the insertion of procedure into human knowledge and social experience. What makes something algorithmic is that it is produced by or related to an information system that is committed (functionally and ideologically) to the computational generation of knowledge or decisions.”

The following is a draft of an essay, eventually for publication as part of the Digital Keywords project (Ben Peters, ed). This and other drafts will be circulated on Culture Digitally, and we invite anyone to provide comment, criticism, or suggestion in the comment space below. We ask that you please do honor that it is being offered in draft form — both in your comments, which we hope will be constructive in tone, and in any use of the document: you may share the link to this essay as widely as you like, but please do not quote from this draft without the author’s permission. (TLG)


Algorithm — Tarleton Gillespie, Cornell University

In Keywords, Raymond Williams urges us to think about how our use of a term has changed over time. But the concern with many of these “digital keywords” is the simultaneous and competing uses of a term by different communities, particularly those inside and outside of technical professions, who seem often to share common words but speak different languages. Williams points to this concern too: “When we come to say ‘we just don’t speak the same language’ we mean something more general: that we have different immediate values or different kinds of valuation, or that we are aware, often intangibly, of different formations and distributions of energy and interest.” (11)

For “algorithm,” there is a sense that the technical communities, the social scientists, and the broader public are using the word in different ways. For software engineers, algorithms are often quite simple things; for the broader public they name something unattainably complex. For social scientists there is danger in the way “algorithm” lures us away from the technical meaning, offering an inscrutable artifact that nevertheless has some elusive and explanatory power (Barocas et al, 3). We find ourselves more ready to proclaim the impact of algorithms than to say what they are. I’m not insisting that critique requires settling on a singular meaning, or that technical meanings necessarily trumps others. But we do need to be cognizant of the multiple meanings of “algorithm” as well as the type of discursive work it does in our own scholarship.

algorithm as a technical solution to a technical problem

In the scholarly effort to pinpoint the values that are enacted, or even embedded, in computational technology, it may in fact not be the “algorithms” that we need be most concerned about — if what we meant by algorithm was restricted to software engineers’ use the term. For their makers, “algorithm” refers specifically to the logical series of steps for organizing and acting on a body of data to quickly achieve a desired outcome. MacCormick (2012), in an attempt to explain algorithms to a general audience, calls them “tricks,” (5) by which he means “tricks of the trade” more than tricks in the magical sense — or perhaps like magic, but as a magician understands it. An algorithm is a recipe composed in programmable steps; most of the “values” that concern us lie elsewhere in the technical systems and the work that produces them.

For its designers, the “algorithm” comes after the generation of a “model,” i.e. the formalization of the problem and the goal in computational terms. So, the task of giving a user the most relevant search results for their queries might be operationalized into a model for efficiently calculating the combined values of pre-weighted objects in the index database, in order to improve the percentage likelihood that the user clicks on one of the first five results.[1] This is where the complex social activity and the values held about it are translated into a functional interaction of variables, indicators, and outcomes. Measurable relationships are posited as existing between some of these elements; a strategic target is selected, as a proxy for some broader social goal; a threshold is determined as an indication of success, at least for this iteration.

The “algorithm” that might follow, then, is merely the steps for aggregating those assigned values efficiently, or delivering the results rapidly, or identifying the strongest relationships according to some operationalized notion of “strong.” All is in the service of the model’s understanding of the data and what it represents, and in service of the model’s goal and how it has been formalized. There may be many algorithms that would reach the same result inside a given model, just like bubble sorts and shell sorts both put lists of words into alphabetical order. Engineers choose between them based on values such as how quickly they return the result, the load they impose on the system’s available memory, perhaps their computational elegance. The embedded values that make a sociological difference are probably more about the problem being solved, the way it has been modeled, the goal chosen, and the way that goal has been operationalized (Reider).

Of course, simple alphabetical sorting may be a misleading an example to use here. The algorithms we’re concerned about today are rarely designed to reach a single and certifiable answer, like a correctly alphabetized list. More common are algorithms that must choose one of many possible results, none of which are certifiably “correct.” Algorithm designers must instead achieve some threshold of operator or user satisfaction — understood in the model, perhaps, in terms of percent clicks on the top results, or percentage of correctly identified human faces from digital images.

This brings us to the second value-laden element around the algorithm. To efficiently design algorithms that achieve a target goal (rather than reaching a known answer), algorithms are “trained” on a corpus of known data. This data has been in some way certified, either by the designers or by past user practices: this photo is of a human face, this photo is not; this search result has been selected by many users in response to this query, this one has not. The algorithm is then run on this data so that it may “learn” to pair queries and results found satisfactory in the past, or to distinguish images with faces from images without.

The values, assumptions, and workarounds that go into the selection and preparation of this training data may also be of much more importance to our sociological concerns than the algorithm learning from it. For example, the training data must be a reasonable approximation of the data that algorithm will operate on in the wild. The most common problem in algorithm design is that the new data turns out not to match the training data in some consequential way. Sometimes new phenomena emerge that the training data simply did not include and could not have anticipated; just as often, something important was overlooked as irrelevant, or was scrubbed from the training data in preparation for the development of the algorithm.

Furthermore, improving an algorithm is rarely about redesigning it. Rather, designers will “tune” an array of parameters and thresholds, each of which represents a tiny assessment or distinction. In search, this might mean the weight given to a word based on where it appears in a webpage, or assigned when two words appear in proximity, or given to words that are categorically equivalent to the query term. These values have been assigned and are already part of the training data, or are thresholds that can be dialed up or down in the algorithm’s calculation of which webpage has a score high enough to warrant ranking it among the results returned to the user.

Finally, these exhaustively trained and finely tuned algorithms are instantiated inside of what we might call an application, which actually performs the functions we’re concerned with. For algorithm designers, the algorithm is the conceptual sequence of steps, which should be expressible in any computer language, or in human or logical language. They are instantiated in code, running on servers somewhere, attended to by other helper applications (Geiger 2014), triggered when a query comes in or an image is scanned. I find it easiest the think about the difference between the “book” in your hand and the “story” within it. These applications embody values as well, outside of their reliance on a particular algorithm.

To inquire into the implications of “algorithms,” if we meant what software engineers mean when they use the term, could only be something so picky as investigating the political implications of using a bubble sort or a shell sort — setting aside bigger questions like why “alphabetical” in the first place, or why train on this particular dataset. Perhaps there are lively insights to be had about the implications of different algorithms in this technical sense,{2] but by and large we in fact mean something else when we talk about algorithms as having “social implications.”

algorithm as synecdoche

While it is important to understand the technical specificity of the term, “algorithm” has now achieved some purchase in the broader public discourse about information technologies, where it is typically used to mean everything described in the previous section, combined. As Goffey puts it, “Algorithms act, but they do so as part of an ill-defined network of actions upon actions.” (19) “Algorithm” may in fact serve as an abbreviation for the sociotechnical assemblage that includes algorithm, model, target goal, data, training data, application, hardware — and connect it all to a broader social endeavor. Beyond the technical assemblage there are people at every point: people debating the models, cleaning the training data, designing the algorithms, tuning the parameters, deciding on which algorithms to depend on in which context. “These algorithmic systems are not standalone little boxes, but massive, networked ones with hundreds of hands reaching into them, tweaking and tuning, swapping out parts and experimenting with new arrangements… We need to examine the logic that guides the hands.” (Seaver 2013) Perhaps “algorithm” is just the name for one kind of socio-technical ensemble, part of a family of authoritative systems for knowledge production or decision-making: in this one, humans involved are rendered legible as data, are put into systematic / mathematical relationships with each other and with information, and then are given information resources based on calculated assessments of them and their inputs.

But what is gained and lost by using “algorithm” this way? Calling the complex sociotechnical assemblage an “algorithm” avoids the need for the kind of expertise that could parse and understand the different elements; a reporter may not need to know the relationship between model, training data, thresholds, and application in order to call into question the impact of that “algorithm” in a specific instance. It also acknowledges that, when designed well, an algorithm is meant to function seamlessly as a tool; perhaps it can, in practice, be understood as a singular entity. Even algorithm designers, in their own discourse, shift between the more precise meaning, and using the term more broadly in this way.

On the other hand, this conflation risks obscuring the ways in which political values may come in elsewhere than at what designers call the “algorithm.” This helps account for the way many algorithm designers seem initially surprised by the interest of sociologists in what they do — because they may not see the values in their “algorithms” (precisely understood) that we see in their algorithms (broadly understood), because questions of value are very much bracketed in the early decisions about how to operationalize a social activity into a model and into the miniscule, mathematical moments of assigning scores and tuning thresholds.

In our own scholarship, this kind of synecdoche is perhaps unavoidable. Like the journalists, most sociologists do not have the technical expertise or the access to investigate each of the elements of what they call the algorithm. But when we settle uncritically on this shiny, alluring term, we risk reifying the processes that constitute it. All the classic problems we face when trying to unpack a technology, the term packs for us. It becomes too easy to treat it as a single artifact, when in the cases we’re most interested in it’s rarely one algorithm, but many tools functioning together, sometimes different tools for different users.[3] It also tends to erase the people involved, downplay their role, and distance them from accountability. In the end, whether this synecdoche is acceptable depends on our intellectual aims. Calling all these social and technical elements “the algorithm” may give us a handle with which to grip we want to closely interrogate; at the same time it can produce a “mystified abstraction” (Striphas 2012) that, for other research questions, it might be better to demystify.

algorithm as talisman

The information industries have found value in the term “algorithm” in their public-facing discursive efforts as well. To call their service or process an algorithm is to lend a set of associations to that service: mathematical, logical, impartial, consistent. Algorithms seem to have a “disposition towards objectivity” (Hillis et al 2013: 37); this objectivity is regularly performed as a feature of algorithmic systems. (Gillespie 2014) Conclusions that can be described as having been generated by an algorithm come with a powerful legitimacy, much the way statistical data bolsters scientific claims, with the human hands yet another step removed. It is a very different kind of legitimacy than one that rests on the subjective expertise of an editor or a consultant, though it is important not to assume that it trumps such claims in all cases. A market prediction that is “algorithmic” is different from a prediction that comes from an expert broker highly respected for their expertise and acumen; a claim about an emergent social norm in a community generated by an algorithm is different from one generated ethnographically. Each makes its own play for legitimacy, and implies its own framework for what legitimacy is (quantification or interpretation, mechanical distance or human closeness). But in the context of nearly a century of celebration of the statistical production of knowledge and longstanding trust in automated calculation over human judgment, the algorithmic does enjoy a particular cultural authority.

More than that, the term offers the corporate owner a powerful talisman to ward off criticism, when companies must justify themselves and their services to their audience, explain away errors and unwanted outcomes, and justify and defend the increasingly significant roles they play in public life. (Gillespie 2014) Information services can point to “the algorithm” as having been responsible for particular results or conclusions, as a way to distance those results from the providers. (Morozov, 2013: 142) The term generates an entity that is somehow separate, the assembly line inside the factory, that can be praised as efficient or blamed for mistakes.

The term “algorithm” is also quite often used as a stand-in for its designer or corporate owner. When a critic says “Facebook’s algorithm” they often mean Facebook and the choices it makes, some of which are made in code. This may be another way of making the earlier point, that the singular term stands for a complex sociotechnical assemblage: Facebook’s algorithm really means “Facebook,” and Facebook really means the people, things, priorities, infrastructures, aims, and discourses that animate them. But it may also be a political economic conflation: this is Facebook acting through its algorithm, intervening in an algorithmic way, building a business precisely on its ability to construct complex models of social/expressive activity, train on an immense corpus of data, tune countless parameters, and reach formalized goals extremely efficiently.

Maybe saying “Facebook’s algorithm” and really meaning the choices and interventions made by Facebook the company into our social practices is a way to assign accountability (Diakopoulos 2013, Ziewitz 2011). It makes the algorithm theirs in a powerful way, and works to reduce the distance some providers put between “them” (their aims, their business model, their footprint, their responsibility) and “the algorithm” (as somehow autonomous from all that). On the other hand, conflating the algorithmic mechanism and the corporate owner may obscure the ways these two entities are not always aligned. It is crucial that we discern between things done by the algorithmic system and things done in other ways, such as the deletion of obscene images from a content platform, which is sometimes handled algorithmically and sometimes performed manually. (Gillespie 2012b) It is crucial to note slippage between a provider’s financial or political aims and the way the algorithmic system actually functions. And conflating algorithmic mechanism and corporate owner misses how some algorithmic approaches are common to multiple stakeholders, circulate across them, and embody a tactic that exceeds any one implementation.

algorithmic as committed to procedure

In recent scholarship on the social significance of algorithms, it is common for the term to appear not as a noun but as an adjective. To talk about “algorithmic identity” (Cheney-Lippold), “algorithmic regulation” (O’Reilly), “algorithmic power” (Bucher), “algorithmic publics” (Leavitt), “algorithmic culture” (Striphas, 2010) or the “algorithmic turn (Uricchio, 2011) is to highlight a social phenomenon that is driven by and committed to algorithmic systems — which include not just algorithms themselves, but also the computational networks in which they function, the people who design and operate them, the data (and users) on which they act, and the institutions that provide these services.

What we are really concerned with when we invoke the “algorithmic” here is not the algorithm per se but the insertion of procedure into human knowledge and social experience. What makes something algorithmic is that it is produced by or related to an information system that is committed (functionally and ideologically) to the computational generation of knowledge or decisions. This requires the formalization of social facts into measurable data and the “clarification” (Cheney-Lippold) of social phenomena into computational models that operationalize both problem and solution. These are often proxies for human judgment or action, meant to simulate it as nearly as possible. But the “algorithmic” intervenes in terms of step-by-step procedures that one (computer or human) can enact on this formalized information, such that it can be computed. This process is automated so that it can happen instantly, repetitively, and across many contexts, away from the guiding hand of its implementers. This is not the same as suggesting that knowledge is produced exclusively by a machine, abstracted from human agency or intervention. Information systems are always swarming with people, we just can’t always see them. (Downey, 2014; Kushner 2013) And an assembly line might be just as “algorithmic” in this sense of the word, or at least the parallels are important to consider. What is central is the commitment to procedure, and the way procedure distances its human operators from both the point of contact with others and the mantle of responsibility for the intervention they make. It is a principled commitment to the “if/then” logic of computation.

Yet what does “algorithmic” refer to, exactly? To put it another way, what is it that is not “algorithmic”? What kind of “regulation” is being condemned as insufficient when Tim O’Reilly calls for “algorithmic regulation”? It would be all too easy to invoke the algorithmic as simply the opposite of what is done subjectively or by hand, or of what can only be accomplished with persistent human oversight, or of what is beholden to and limited by context. To do so would draw too stark a contrast between the algorithm and something either irretrievably subjective (if we are glorifying the impartiality of the algorithmic) or warmly human (if we’re condemning the algorithmic for its inhumanity). If “algorithmic” market predictions and search results are produced by a complex assemblage of people, machines, and procedures, what makes their particular arrangement feel different than other ways of producing information, which are also produced by a complex assemblage of people, machines, and procedures, such that it makes sense to peg them as “algorithmic?” It is imperative to look closely at those pre- and non-algorithmic practices that precede or stand in contrast to those we posit as algorithmic, and recognize how they too strike a balance between the procedural and the subjective, the machinic and the human, the measured and the ineffable. And it is crucial that we continue to examine algorithmic systems and their providers and users ethnographically, to explore how the systemic and the ad hoc coexist and are managed within them.

To highlight their automaticity and mathematical quality, then, is not to contrast algorithms to human judgment. Instead it is to recognize them as part of mechanisms that introduce and privilege quantification, proceduralization, and automation in human endeavors. Our concern for the politics of algorithms is an extension of worries about Taylorism and the automation of industrial labor; to actuarial accounting, the census, and the quantification of knowledge about people and populations; and to management theory and the dominion of bureaucracy. At the same time, we sometimes wish for more “algorithmic” interventions when the ones we face are discriminatory, nepotistic, and fraught with error; sometimes procedure is truly democratic. I’m reminded of the sensation of watching complex traffic patterns from a high vantage point: it is clear that this “algorithmic” system privileges the imposition of procedure, and users must in many ways accept it as a kind of provisional tyranny in order to even participate in such a complex social interaction. The elements can only be known in operational terms, so as to calculate the relations between them; every possible operationalized interaction within the system must be anticipated; and stakeholders often point to the system-ness of the system to explain success and explain away failure. The system always struggles with the tension between the operationalized aims and the way humanity inevitably undermines, alters, or exceeds those aims. At the same time, it’s not clear how to organize such complex behavior in any other way, and still have it be functional and fair. Commitment to the system and the complex scale at which it is expected to function makes us beholden to the algorithmic procedures that must manage it. From this vantage point, algorithms are merely the latest instantiation of the modern tension between ad hoc human sociality and procedural systemization — but one that is now powerfully installed as the beating heart of the network technologies we surround ourselves with and increasingly depend upon.


1. This parallels Kowalski’s well-known definition of an algorithm as “logic + control”: “An algorithm can be regarded as consisting of a logic component, which specifies the knowledge to be used in solving problems, and a control component, which determines the problem-solving strategies by means of which that knowledge is used. The logic component determines the meaning of the algorithm whereas the control component only affects its efficiency.” (Kowalksi, 424) I prefer to use “model” because I want to reserve “logic” for the underlying premise of the entire algorithmic system and its deployment.

2.See Kockelman 2013 for a dense but superb example.

3.See Brian Christian, The A/B Test: Inside the Technology That’s Changing the Rules of Business.” Wired, April 25.


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