Unsurprisingly, perhaps, algorithms are dificult to represent or culturally mediate even for computer scientists.11 However, as Sandvig (2015) notes, in an atempt to make the algorithm more intelligible to the public we are seeing an epistemic development in which “algorithms now have their own public relations.” That is, beyond the technical or educational imagery of algorithms characteristic of earlier times, algorithms materialize in diferent representational setings. Algorithms have become objects of marketing, represented in commercialized images of mathematical formulas and circulated by various third-party marketing consultants, technology blogs, and other trade press publications. The process of making algorithms a product of public relations is not so diferent from what Charles Bazerman (2002) calls the production of “representational resting points.” In examining the cultural history of Edison’s electric light, Bazerman argues that, before the invention “could create its own new world of experience and meaning,” it was crucial to develop accepted representations of electric light as stable points of reference (2002: 320).12 What exactly these representational resting points are with regards to algorithms and machine learning is still up for grabs, but simplified mathematical formulas or cyborg figures are certainly part of it. Though the formula may portray algorithms as objects of science and neutral conduits, this is arguably not what people experience in their day-to-day life. As the stories of Michael and Rachel suggest, what people experience is not the mathematical recipe but, rather, the moods, afects, and sensations of which algorithms are generative. The notion of mental models, then, only brings us so far—not because the theories that people construct about unfamiliar technologies and hidden processes often prove to be inaccurate (Adams & Sasse, 1999; Wash, 2010), but because it does not necessarily mater whether they are correct or not.
Affective Landscapes
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