Their multiplicity, moreover, pertains to their constantly changing nature. As we saw in the previous chapter, Facebook is routinely described as a work in progress. This is not just to be understood as innovation speak but, quite literally, as an integrated business plan constantly to improve its algorithms in order to retain users. This ethic of continuous change is by no means unique to Facebook: It is part and parcel of how most online platforms now develop their products. So-called A/B testing is integral to the culture of experimentation that governs platforms as a means for assessing and comparing how well diferent versions of the algorithm perform.8 A/B tests basically work like focus groups in which people are asked for their opinion on a certain product—with the important diference that most people participating in an A/B test are not aware of it. As Wired reporter Brian Christian (2012) explains:
Without being told, a fraction of users are diverted to a slightly diferent version of a given web page and their behavior compared against the mass of users on the standard site. If the new version proves superior—gaining more clicks, longer visits, more purchases—it will displace the original; if the new version is inferior, it’s quietly phased out without most users ever seeing it.
These “invisible” experimentations have now become such an important part of product development on the Web that, for a company like Netflix, A/B test results are seen as their “most important source of information for making product decisions” (Gomez-Uribe & Hunt, 2015: 11). Typically performed as a test of two variants and on two user groups, A/B tests epitomize the logic of constant change. At any given time, multiples of these tests are executed in parallel. At Netflix, these tests are not just performed on two variants but often include 5 to 10 versions of the algorithm and, typically, run for a period of 2 to 6 months (Gomez-Uribe & Hunt, 2015: 9-12). As a result, there is not one “Netflix” to speak of but many diferent permutations (Seaver, 2013). The culture of experimentation complicates any efort to know algorithms, as the question inevitably arises as to which version, what test group, or what timeframe we are talking about. What I want to suggest, then, is that the algorithms operating in contemporary media platforms are simply neither black nor box but eventful. For a conceptualization of algorithms, this implies a rejection of essences and permanence and an ontological shift towards a world of process and relations.
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IF . . . THEN