[N]egative knowledge is not nonknowledge, but knowledge of the limits of knowing, of the mistakes we make in trying to know, of the things that interfere with our knowing, of what we are not interested in and do not really want to know. (1999: 64)
Unknowing algorithms as a first step in knowing them would be to engage more actively in addressing the things that seem to interfere with or keep us from knowing. Indeed, what I want to argue is that the metaphor of the black box itself constitutes such interference. In our atempts to open the black box, what frequently gets ignored is the question of whether the metaphor of the black box holds at all. It might be easier and more tangible to think of algorithms as black boxes because it allows the analyst, policymaker, or outside critic to call for more transparency and openness as a practical means for making powerbrokers accountable. For all its alleged blackness, the box not only conceals the code the critics want to reveal, it also conceals the fact that algorithms may not even be as black or as boxy as they are often held out to be.
Multiple, Processual, Heterogeneous
RELATIONAL ONTOLOGY AND ALGORITHMS
The central focus in this chapter has to do with the possibilities and challenges of knowing algorithms that are commonly described as black boxes. What I have argued so far is that the metaphor of the black box conceals its own limitations as an epistemic device. By encouraging the Enlightenment impetus of unveiling—of opening—the black box positions visibility as a conduit for knowledge and control. Algorithms, though, are not stand-alone boxes but always part of complex systems. They often operate as a collection of algorithms in what are, ultimately, networked systems. This is particularly true for the algorithms underlying platforms such as Facebook, Twiter, Netflix, or YouTube. These systems do not contain one algorithm but a collection of algorithms, working together to create a unified experience. As product owners at Netflix, Gomez-Uribe and Hunt write, “our recommender system consists of a variety of algorithms that collectively define the Netflix experience” (2015: 2). To give an impression of the many algorithms at play in designing this overall Netflix experience, Gomez-Uribe and Hunt list at least eight diferent algorithms, including the personalized video ranker (PVR), which orders the entire catalogue of videos for each member in a personalized way; the Top-N Video ranker, which produces the recommendations in the Top Picks; and the page generation algorithm, which works to construct every single page of recommendations. Similarly, YouTube’s video recommendation system is not powered by one single algorithm but many, including the related/recommended video suggestion algorithm, search algorithms, and Content ID (Davidson et al., 2010). Facebook is no exception. As technology reporter Will Oremus (2016) writes in Slate: “the algorithm is really a collection of hundreds of smaller algorithms solving the smaller problems that make up the larger problem of what stories to show people.”
Neither Black nor Box
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