{"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":58,"pdf_page":68,"physical_index":68,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"13f72c346bdfb2ade0d6e91c55cb3816dfa1b9ae5e74f820cfdc0126693cb4bf"},"chapter":"IF...THEN","content":"What we need to remember is that all of these layers and structures are built by people in teams, and so the ability for one person to understand everything is very much the same challenge as understanding how the University of Copenhagen works. Yet at the same time teams can build heuristic understanding of their algorithmic systems, feelings of what might cause it to break or what is causing the bottleneck, which allow them to work and do their job.23\n\nThe ignorance of higher-placed experts, however, should not detract us from knowing diferently. Now, if the exact configuration of the algorithmic logic cannot be easily traced—say, for example, by examining what the machine learned at diferent layers in a neural net, it should not keep us from interrogating the oddity itself, particularly since the allusion to ignorance often comes in handy for the platforms themselves. As Christian Sandvig (2015) suggests, “platform providers often encourage the notion that their algorithms operate without any human intervention, and that they are not designed but rather ‘discovered’ or invented as the logical pinnacle of science and engineering research in the area.” When things do not go exactly as planned and platforms are accused of censorship, discrimination or bias, the algorithm is often conveniently enlisted as a strategic unknown. Of course, as Callon and Latour aptly remind us, “black boxes never remain fully closed or properly fastened [ . . . ] but macro-actors can do as if they were closed and dark” (1981: 285). It is certainly odd that a particular purchase is deemed suspicious, or that a credit card company cannot explain why an algorithm came to this or that conclusion. However, the knowledge of an algorithmic event does not so much hinge on its causes as it does on the capacity to produce certain “orderings and disorderings” (Michael, 2004). As an event whose identity is uncertain, there is an opportunity to ask new questions. In the case of the credit card company, the question might not necessarily be phrased in terms of why the algorithm came to a certain conclusion but what that conclusion suggests about the kinds of realities that are emerging because people are using algorithms and what these algorithmic practices do to various people.\n\nIn terms of transparency, questions also need to be asked of functions and expectations. When the Norwegian newspaper Aftenposten and Norway’s Prime Minister Erna Solberg called out Facebook in September 2016, accusing its algorithms of censoring the iconic Pulitzer Prize image “Terror of War,” they did so because they expected the algorithm to behave in a specific way. An algorithm, they suggested, should have been able to distinguish between an award-winning iconic image and “normal nudity.” Not only was this incident, to which I shall return later in chapter 6, yet another example of the diferential enactment of agency as exemplified in the question of whether the fault was with the algorithms or the humans: It also shows that, sometimes, what there is to know about algorithms may not be about the algorithm itself but, rather, our own limits of understanding. Should we, perhaps, not just worry about algorithms being wrong but also ask whether they do what they are supposed to do? Why was Facebook called out on censoring an image of a naked girl when, in fact, this is what is expected of them? Knowing algorithms, I want to suggest, may be just as much about interrogating “negative knowledge” in Knorr-Cetina’s sense as it is about trying to peel the layers of a neural network or geting access to the actual source code. In other words, when trying to know algorithms we also have to take into account what things interfere with our knowing, what we are not interested in, what we do not want to know and why.\n\n58\n\nIF . . . THEN","previous_page":57,"next_page":59}