{"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":23,"pdf_page":33,"physical_index":33,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"ebfbb47b6b82efca2531802bfe1af9b725d4cedca5d251591bfe37b8790f1e0b"},"chapter":"IF...THEN","content":"Wirth defines as algorithms– and the social and cultural implication those operations have, the focus in this book will almost exclusively be on algorithms.\n\nWhile no agreed-upon definition of algorithm exists, a few aspects have been described as important characteristics. For Donald Knuth (1998), who has writen one of the most important multivolume works on computer programming, algorithms have five broadly defined properties: finiteness, definiteness, input, output, and efectiveness. What is generally asked of an algorithm is that it produce a correct output and use resources eficiently (Cormen, 2013). From a technical standpoint, creating an algorithm is about breaking the problem down as eficiently as possible, which implies a careful planning of the steps to be taken and their sequence.\n\nTake the problem of sorting, which is one of the most common tasks algorithms are deployed to solve. A given sorting problem may have many solutions; the algorithm that eventually gets applied is but one possible solution. In other words, an algorithm is a manifestation of a proposed solution. Just as there are multiple ways of sorting a bookshelf in some well-defined order—for example, according to alphabetical order by the author’s surname, by genre, or even by the color of the book jacket, diferent sorting algorithms (e.g., selection sort, merge sort, or quicksort) can be applied for the same task. Anyone who has ever tried to arrange a bookshelf according to the color of the book jacket will probably be able to understand how this specific organizational logic might have an aesthetically pleasing efect but also come with the added practical challenge of finding a particular book by a certain author (unless you have an excellent color memory). This is to say that algorithms, understood as forms of organizational logic, come with specific afordances that both enable and constrain. To use a well-known insight from science and technology studies (STS), such orderings are never neutral. Algorithms come with certain assumptions and values about the world on which they are acting. Compared to the archetypical example used in STS about the inherent politics of engineering and urban planning in the example of Robert Moses’ low-hanging bridges described by Langdon Winner (1986), the politics and values of algorithms are a really blatant example of that same logic as they explicitly decide, order, and filter the world in specific ways. Why some people still think of algorithms as neutral, given they are a relatively easy example of an STS value-laden technology, is a diferent question altogether. Ultimately, algorithms and the models used have consequences. In the example of the bookshelf, a color coding scheme might, for instance, imply that I might become less inclined to read books by the same author simply because I did not facilitate my thinking or decision-making process in terms of genre or author in the first place.\n\nT H E L E A R N I N G T YP E O F A L G O R I T H M\n\nAn important distinction needs to be made between algorithms that are preprogrammed and behave more or less deterministically and algorithms that have the ability to “learn” or improve in performance over time. Given a particular input, a deterministic algorithm will always produce the same output by passing through the same sequence of steps. The learning type, however, will learn to predict outputs based on previous examples of relationships between input data and outputs. Unlike a deterministic algorithm that correctly sorts an alphabetized list, many of the algorithms that run the Internet today do not necessarily have one easily definable, correct result. The kinds of algorithms and techniques to which I am referring here are called machine learning, which is essentially the notion that we can now program a computer to learn by itself (Domingos, 2015). In contrast to the strict logical rules of traditional programming, machine learning is about writing programs that learn to solve the problem from examples. Whereas a programmer previously had to write all the “if. . . then” statements in anticipation of an outcome herself, machine learning algorithms let the computer learn the rules from a large number of training examples without being explicitly programmed to do so. In order to help reach a target goal, algorithms are “trained” on a corpus of data from which they may “learn” to make certain kinds of decisions without human oversight. Machines do not learn in the same sense that human do, although many of the algorithms used are based on artificial neural networks are inspired by the structure and functional aspects of  the brain’s deep architecture (Hecht-Nielsen, 1988). The kind of learning that machines do should be understood in a more functional sense: “They are capable of  changing their behavior to enhance their performance on some task through experience” (Surden, 2014: 89).\n\nTh e M u l tip l icity of A lgo rith ms\n\n23","previous_page":22,"next_page":24}