Following Ashby’s cybernetic lead, what is at stake in a technographic inquiry is not to reveal some hidden truth about the exact workings of software or to unveil the precise formula of an algorithm. Instead the aim is to develop a critical understanding of the mechanisms and operational logic of software. As Galloway states, the question is “how it works” and “who it works for” (2004: xiii). Just as the ethnographer observes, takes notes, and asks people about their beliefs and values, Ashby’s observer and the technographer describe what they see and what they think they see. The researcher confronted with the black box algorithm does not necessarily need to know much about code or programming (although it is certainly an advantage). As Ashby points out, “no skill is called for! We are assuming, remember, that nothing is known about the Box” (1999: 89).
In general, we might say that a good way to start is by confronting known unknowns in terms of one’s own limits of knowledge. What, for example, is there to know about key concepts in computer science, mathematics, or social sciences that would be useful for understanding a specific algorithmic context? Then, we also have the option of tracing the many “semiotic systems that cluster around technical artefacts and ensembles” (Mackenzie, 2002: 211): patent applications and similar documents that detail and lay out technical specifications, press releases, conference papers on machine learning techniques, recorded documents from developer and engineering conferences, company briefs, media reports, blog posts, Facebook’s IPO filing, and so on. Finally, we might want to experiment with systems as best we can or even code them ourselves. As the next chapter will show, a technography of algorithms need not imply elaborate experimentation or detailed technical knowledge but, above all, a readiness to engage in unknown knowns, seeing the black box not as an epistemological obstacle but as a playful challenge that can be described in some ways (but not all).
PHENOMENOLOGICAL ENCOUNTERS WITH UNKNOWN KNOWNS
The second tactic suggests another way of knowing algorithms, which is to bring sense to what we do not know but know nonetheless. From a phenomenological perspective, approaching algorithms is about being atentive to the ways in which social actors develop more or less reflexive relationships to the systems they are using and how those encounters, in turn, shape online experience. Drawing on concepts such as “tacit knowledge,” “nonknowledge,” and “practical knowledge,” a phenomenological approach to algorithms is concerned with understanding how algorithms are perceived and made sense of by the actors in a given situation. Positing the algorithm as an unknown known in this case is meant to highlight the productive force of personal forms of knowledge, knowledge that is gained through experience and practical engagement with one’s lived-in environments (Ingold, 2000). Although algorithms, in the strict technical and mathematical sense of the term, may remain unknowns, there are tacit forms of knowledge that linger in the background and which may have as much of an impact on the ways in which life with algorithms take shape as the coded instructions themselves. When long-lost friends suddenly show up on your Facebook news feed or a hotel that you have been looking at on a booking site materializes as an ad and seems to follow you wherever else you go on the Web, or Netflix just seems to be “reading your mind” in terms of the kind of movie you are in the mood to watch, algorithms become locally available for analysis in terms of the kinds of experiences and afects they engender.
Neither Black nor Box
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