{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"1d3bd28e-c1ba-4af2-8580-60b32ef26053","title":"If...Then Algorithmic Power and Politics","author":null,"edition":null,"publisher":null,"year":null,"isbn":"2017054909","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"1d3bd28e-c1ba-4af2-8580-60b32ef26053","id":"1d3bd28e-c1ba-4af2-8580-60b32ef26053","uuid":"1d3bd28e-c1ba-4af2-8580-60b32ef26053","title":"If...Then Algorithmic Power and Politics","normalized_title":"ifthenalgorithmicpowerandpolitics","author":null,"edition":null,"publisher":null,"year":null,"isbn":"2017054909","isbns":["2017054909","9780190493042","9780190493059"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":211,"navigation_url":"https://workshop.closeclaw.top/ai/book/1d3bd28e-c1ba-4af2-8580-60b32ef26053","title_url":"https://workshop.closeclaw.top/ai/title/If...Then%20Algorithmic%20Power%20and%20Politics","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/1d3bd28e-c1ba-4af2-8580-60b32ef26053/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/1d3bd28e-c1ba-4af2-8580-60b32ef26053/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/1d3bd28e-c1ba-4af2-8580-60b32ef26053","page_url":"https://workshop.closeclaw.top/api/ai/books/1d3bd28e-c1ba-4af2-8580-60b32ef26053/page","search_url":"https://workshop.closeclaw.top/api/ai/books/1d3bd28e-c1ba-4af2-8580-60b32ef26053/search"},"page":{"printed_page":53,"pdf_page":63,"physical_index":63,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"fb87acc93daad83d8c2b89f7f8b029f788ccea918f7277d59b8cdee6351eb3dc"},"chapter":"IF...THEN","content":"Of course, with machine-learning algorithms, some would, perhaps, be tempted to argue that, because the engineers or designers of the system are not necessarily human, concerns over the influence of human values or bias becomes less of a problem. However, just as algorithms may “inherit the prejudices of prior decision-makers,” they may “reflect the widespread biases that persist in society at large” (Barocas & Selbst, 2016: 1). To understand where bias might be located in the case of the Facebook trending topic controversy, it certainly helps to know something about how the trending topic algorithm works. It helps to know, for instance, that, at the time of the controversy, Facebook relied on a few news outlets to determine whether “a subject is newsworthy or not” (Stat, 2016). This meant that heavy hiters such as the New York Times and CNN—traditional media institutions—played a significant part in determining whether something would actually be considered a trend or not. As suggested by the leaked internal documents used by Facebook to guide the work of its trending topic editors, “The document designates 10 outlets as particularly central, and instructs editors to only label a topic as a ‘National Story’ or ‘Major Story’ based on how many of those publications placed the news on their front page” (Brandom, 2016). These guidelines and the information about the work practices of editors and the types of decisions they have to make when faced with labeling something as particularly newsworthy or not sheds important light on the diferent values, mechanisms, and ideologies at play in a seemingly neutral and objective decision-making process. Furthermore, it helps to know how central a role users have in shaping algorithmic outcomes. As reporter Ezra Klein puts it, the user is “Facebook’s most biased curator” (Klein, 2016). Users mater because it is their data, their clicking behavior, preferences, network relations, and communicative actions that provide the data for algorithms to act on.\n\nAlthough Facebook’s news feed is often heralded as a prime example of algorithmic finesse, the feed is far from simply automatic or without human intervention. As with the trending section, Facebook enlists human intervention of the news feed as well. During the summer of 2014, Facebook set up a “feed quality panel,” a group of several hundred people located in Knoxville, Tennessee, whom the company paid  to provide detailed feedback on what they were seeing in their news feed (Oremus, 2016). Later, Facebook took its panel nationwide, paying a representative sample of users to rate and review their feeds on a daily basis. They even expanded the panel overseas. More recently, Facebook has been running a survey asking a subset of users to choose between two posts that are shown side-by-side and pick the one that appeals to them the most. Facebook is not the only platform that “humanizes” its algorithmic systems. Netflix, for example, employs a wide range of “taggers,” whose responsibility it is to assess the genre, tone, and style of a film’s content to help determine what users might want to watch next. Humans are also in the loop when it comes to music recommendations in which music is often presented as belonging to a special category of content that computers cannot necessarily “understand.” One of the selling points of Apple Music, the music-streaming service developed by Apple and launched in the summer of 2015, was the fact that it relied heavily on human curators and radio hosts to provide the recommendations as opposed to mere algorithms. In an interview with the Wall Street Journal, Apple CEO Tim Cook claimed, “People love the human curation” aspect of the streaming service (WSJ,  2015). Similarly, Ajaj Kalia, product head of Taste Profiles at Spotify, thinks that for “something as emotional as music [ . . . ] it is crucial to keep humans in the loop” (Popper, 2015). Because computers “cannot truly appreciate music,” Spotify employs a team of 32 music experts around the world to curate playlists that are updated on a weekly basis (Knight, 2015). Around the same time that Apple Music was launched, Spotify introduced Discover Weekly, an algorithmically generated playlist that would get closer to cracking the emotional standard of human taste. The algorithms behind Discover Weekly provide users with a recommended playlist every Monday, based on the user’s unique taste profile, compiled by comparing her tastes to the playlists of other users who have featured the same songs and artists that she liked (Heath, 2015; Pasick, 2015). Then, the algorithm goes through the other songs that the related people have added to their playlists but the target user has not yet heard, based on the assumption that there is a good chance she might like them, too (Popper, 2015). Mathew Ogle, who oversees Discover Weekly, describes this process as one of “magic filtering” (Pasick,  2015). However, algo rithms are just one aspect to this magic. As the majority of media reports on Discover Weekly emphasize, the main ingredient is other people and their curated playlists. Reminiscent of what seems to be a broader rhetorical strategy surrounding big data and algorithms on part of media platforms, Ogle insists that thinking of Discover Weekly in terms of algorithms misses the point since the “whole thing is built on data created by humans— it's just that algorithms are connecting the dots on a massive scale [ . . . ] Discover Weekly is humans all the way down” (Heath, 2015, my emphasis). These examples do not merely show how humans are always already implicated in algorithms, whether as end-users providing the data input from which algorithms learn or the human experts employed to help algorithms compute things such as musical taste or the tone of a film, they are also meant to illustrate the pothole of atributing agency to either an algorithm or a human. The point here is not that we need to choose to whom or what agency most obviously belongs. It is not that agency is with the designers, the users or the algorithm. A relational perspective precisely “disavows any essentialist or isolated explanation of either human or nonhuman agency” (Schubert, 2012: 126). Rather, it is a mater of accounting for the manner in which the assemblage or coming together of entities becomes more or less human, more or less nonhuman. As it was suggested in the previous chapter, algorithms have a variable ontology, meaning that the question of where agency operates cannot be conclusively answered. What needs explaining, then, is the continuum of variation. In the following, I want to suggest that one way of answering what makes an algorithm more or less technical/nonhuman or more or less social/human can be done by shifting atention away from questions of where agency is located to questions of when agency is mobilized and on whose behalf.\n\nNeither Black nor Box\n\n53","previous_page":52,"next_page":54}