{"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":31,"pdf_page":41,"physical_index":41,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"57b2cfb1e6632159ac93c8cbeadeb9eb0748d3c7476ef92719d782a7b3cf5f31"},"chapter":"IF...THEN","content":"The notion of algorithm as adjective suggests that, when social scientists and humanities scholars invoke the term “algorithmic,” what they are really concerned with “is not the algorithm per se but the insertion of procedure into human knowledge and social experience” (Gillespie,  2016a: 25). The insertion of procedurality and quantification into human experience has been noted in various recent accounts on  culture, aesthetics, and knowledge production. Cultural theorist Ted Striphas (2015), for example, writes about the emergence of an “algorithmic culture,” which he takes to be the ways in which platforms such as Netflix and Amazon are seen to alter the way in which culture has traditionally been practiced, experienced, and understood.20 Furthermore, Mark Lenglet (2011) describes how the financial world has become algorithmic not only by having algorithmic procedures inserted into trading but also through the ways in which algorithms now occupy the minds of traders, making them act and react in certain ways. As I will discuss in chapter 5 and 6, this is not unique to the financial world but is evident in everything from social media users orientation toward platforms through to the institutional contexts of journalism where algorithms are inserted into both journalistic practices and discourse. Even identity seems to have become algorithmic, as evidenced by the “shift from ofline to online marketing” (Cheney-Lippold, 2011: 175). As seen from the perspective of many sociologists, cultural theorists, and media scholars, what is interesting about algorithms is not necessarily the same as what interests a computer scientist or an engineer. While an algorithm is still a set of instructions used to solve a computational problem, the valences of the term obviously difer in mathematics and sociology. However, as Gillespie points out, “to highlight the mathematical quality is not to contrast algorithms to human judgement. Instead it is to recognize them as part of mechanisms that introduce and privilege quantification, proceduralization, and automation in human endeavors” (2016a: 27).\n\nThe social concerns voiced about algorithms today are not necessarily new. As mechanisms of quantification, classification, measurement, and prediction, algorithms are as much imbued in the history of computation and software engineering as they are in the history of statistics, accounting, and bureaucratization. As such, the historical and cultural contexts of algorithms intersect with the social history of calculation and ordering of various types, including the history and politics of statistical reasoning and large numbers (Desrosieres & Naish, 2002; Foucault, 2007; Hacking,  2006; Power,  2004); practices of quantification, numbering and valuation (Callon & Law, 2005; Espeland & Stevens, 2008; Verran, 2001); the cultural logic of rankings and ratings (Espeland & Sauder, 2007; Sauder & Espeland, 2009); and ideas of critical accounting and auditing (Power,  1999; Strathern,  2000b). Moreover, contemporary concerns about the power of algorithms can also be seen as an “extension of worries about Taylorism and the automation of industrial labor” (Gillespie, 2016a: 27) or “the century long exercise by media industries to identify (and often quantify) what’s popular” (Gillespie, 2016b). Clearly, algorithms need to be understood as part of a historical lineage. Indeed, we might say that the “avalanche of numbers” (Hacking, 1991, 2015), which occurred as nation-states started to classify and count their populations in the 19th century, forms a general backdrop for an understanding of algorithms in the era of big data. This specific historical lineage, however, if we were to carry it all the way through, would also require us to travel a complex and disjunctive route via the social history of census, punch cards, bureaucratization and the rise of industrial society and factory work, wartime machinery and the rise of computers, databases and the automated management of populations, before arriving at the contemporary milieu of “making up people” (Hacking, 1999) through data mining and machine learning techniques. This is the genealogy that tells the story of managing populations—most notably theorized by  Michel Foucault in his College de France lectures (Foucault,  2007,  2008). As Foucault (2007: 138) points out, statistics means etymologically “knowledge of the state.” The task for those who govern, then, is to determine what one needs to know in order to govern most efectively and how that knowledge is to be organized. If the collection of data through the numbering practices of statistics and the corporate data mining of recent years makes algorithmic operations possible, the algorithms themselves (understood in the technical sense) give shape to otherwise meaningless data. While the significant power and potential of big data (the quantity of information produced by people, things, and their interactions) cannot be denied, its value derives not from the data themselves but from the ways in which they have been brought together into new forms of meaningfulness by the associational infrastructure of the respective software systems in which algorithms play a key role.\n\nTh e M u l tip l icity of A lgo rith ms\n\n31","previous_page":30,"next_page":32}