What is at stake here, then, is not the black box as a thing but, rather, the process of “blackboxing,” of making it appear as if there is a stable box in the first place. If the metaphor of the black box is used too readily as a way of critiquing algorithms, Latour’s notion of blackboxing reminds us that we might want to scrutinize critically the ways diferent actors have a vested interest in figuring the algorithm as a black box in the first place. As discussed in this chapter, the alleged unknowability of algorithms is not always seen as problematic. It can also be strategically used to cultivate an ignorance that is sometimes more advantageous to the actors involved than knowing. By referring to the notion of strategic unknowns as part of this third methodological option, the intention is to point out the deeply political work that goes into the figurations of algorithms as particular epistemic phenomena. In chapter 6, I turn to these questions anew by looking at how algorithms variously become enlisted, imagined and configured as part of journalistic practices and products.
Concluding Remarks
Let this be the general conclusion: For every epistemological challenge the seemingly black-boxed algorithm poses, another productive methodological route may open. The complex and messy nature of social reality is not the problem. Just as algorithms constitute but one specific solution to a computational problem, we cannot expect a single answer to the problem of how to know algorithms. Borrowing from Law, “one thing is sure: if we want to think about the messes of reality at all then we’re going to have to teach ourselves to think, to practice, to relate, and to know in new ways” (2004a: 2). In this chapter the black box was used as a heuristic device to deal with this mess—not by making the world less messy, but by redirecting atention to the messiness that the notion of the black box helps to hide.
Not to be taken as a definitive, exhaustive list of well-meant advice, I ofered three steps to consider when researching algorithms. First, do not regard the “impossibility of seeing inside the black box” as an epistemological limit that impinges any “futile atempts at knowledge acquisition” (von Hilgers 2011: 43). Ask instead what parts can and cannot be known and how, in each particular case, you may find ways to make the algorithm talk. Second, instead of expecting the truth to come out from behind the curtain or to lay there in the box just waiting for our hands to take the lid of, take those beliefs, values, and imaginings that the algorithm solicits as point of departure. Third, keep in mind that the black box is not as seamless as it may seem. Various actors and stakeholders once composed black boxes in a specific historical context for a specific purpose. Importantly, they evolve, have histories, change, and afect and are afected by what they are articulated to. While we often talk about algorithms as if they were single stable artifacts, they are boxed to precisely appear that way.
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