{"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":162,"pdf_page":172,"physical_index":172,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"83a46501082041ef748d8b03e3c6dbc1e009bc35a59073ebb640b1805e5d5756"},"chapter":"IF...THEN","content":"11. Probabilistic models or classifiers called Naive Bayes usually power spam filters. For more on the history and working of Naive Bayes classifiers, see Domingos (2015: 151–153) or Rieder (2017). According to Domingos, it is supposedly the most used learner at Google.\n\n12. See Barocas & Selbst (2016: 9) on the creditworthiness.\n\n13. Rules of thumb and systematic searching through the performance of diferent parameter values on subsets of the data (cross-validation) which are also often used.\n\n14. Numbers based on consulting Facebook’s newsroom on December 31, 2014: htp://newsroom.fb.com/products.\n\n15. Neural networks in machine learning do not work exactly like the brain but are simply inspired by the ways in which the brain is thought to learn. The first formal model of a neuron was proposed by Warren McCulloch and Walter Pits in 1943. It was not until 1957, when Frank Rosenblat pioneered neural nets with his conception of the perceptron, that neurons were thought to be able to learn to recognize simple paterns in images. For more on the early history of neural nets, see Minsky & Papert (1969) and M. Olazaran (1996). For a literature overview of the field of neural networks and deep learning, see, for example, J. Schmidhuber (2015).\n\n16. For more on how Google image recognition works using deep learning and neural networks, see htp://googleresearch.blogspot.dk/2015/06/inceptionism-going-deeper-into-neural.html.\n\n17. Bayes’ theorem sits at the heart of statistics and machine learning, though what a Bayesian means by probabilities may difer from the ways in which statisticians use the concept. As Domingos (2015) explains, statisticians usually follow a much stricter “frequentist” interpretation of probability.\n\n18. For the most comprehensive bibliography on critical algorithm studies, put together by Tarleton Gillespie and Nick Seaver, see: htps://socialmediacollective.org/reading-lists/ critical-algorithm-studies (last accessed, May 2, 2016).\n\n19. The need for coding skills is one of the most discussed topics in software studies. While it certainly helps to know the principles of writing code, some syntax of a programming language, and how the computer works, a sociologist of algorithms needs to know how to code no more than a television scholar needs to know mechanical engineering.\n\n20. On the notion of “algorithmic culture,” see also Galloway (2006b); Kushner (2013).\n\n21. For an overview of Foucault’s concepts of power, see Lemke, 2012; Faubion, 1994; Rabinow, 1994. Foucault’s public lectures (held at the Collége de France as part of his appointment as chair of the “history of systems of thought” between 1970 and his death in 1984) provide a comprehensive survey of his various conceptualizations of power, including the notions of disciplinary power (Foucault, 2015), biopower (Foucault, 2007, 2008), and pastoral power (Foucault, 2007). Foucault’s text on The Subject and Power (1982) provides a classic statement of how he understood the notion of power and how it can be studied.\n\n22. Foucault’s ideas of omnipresent power, however, have a much longer historical philosophical tradition going back, at least, to the philosopher Baruch Spinoza (1632–1677) who is known for his metaphysics of substance monism, the view that everything is a “mode” of one ontological substance (God or Nature). Monism is typically contrasted with Cartesian dualism or the view that the world is made of two fundamental categories of things or principles. For Spinoza, modes (or subsets of the substance) are always in the process of entering into relations with other modes (i.e., humans, animals, things, etc.). As Jane Bennet puts it, Spinozan nature is “a place wherein bodies strive to enhance their power of activity by forging alliances with other bodies in their vicinity” (2004: 353). Foucault’s way of understanding power as omnipresent and relational bears resemblance to Spinoza’s monism and his concept of immanent causality, see Juniper & Jose, 2008; Deleuze, 1988.\n\n23. Gatekeeping is the idea that information flows from senders to receivers through various “gates.” Originally conceived by the social psychologist Kurt Lewin in 1947, the idea of gatekeeping became crucial to journalism studies and information science as a way to explain the process of editing information. In the domain of communication, the gatekeeping concept is usually atributed to Lewin’s research assistant David Manning White, who in 1950 studied how “Mr. Gates,” a wire editor in a small US newspaper, based his selection of news on some highly subjective criteria (Thurman, 2015). Ever since, the notion of gatekeeping has been used to designate the editorial decision-making process in journalism and media.\n\n162\n\nNOTES TO PAGES 26–34","previous_page":161,"next_page":163}