{"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":46,"pdf_page":56,"physical_index":56,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"6fa9e62bca84fffa335189b08955faf380903c67f8506351f5eaed27ad985f09"},"chapter":"IF...THEN","content":"UNKNOWING ALGORITHMS\n\nWhile it is true that proprietary algorithms are hard to know, it does not make them unknowable. Perhaps paradoxically, I want to suggest that, while algorithms are not unknowable, the first step in knowing algorithms is to un-know them. By unknowing, I mean something akin to making the familiar slightly more unfamiliar. In the previous chapter, we saw how algorithms mean diferent things to diferent stakeholders and people from diferent disciplines. This is not to say that a computer scientist needs to dismiss her knowledge of algorithms or that social scientists should somehow dismiss their ways of seeing algorithms as objects of social concern. “Unknowing” also does not imply “blackboxing” the black box even more. Rather, “unknowing” means seeing diferently, looking elsewhere, or not even looking at all. As much as calls for transparency atempt to make the object of concern more visible, visibility, too, may conceal. As Strathern notes, “there is nothing innocent about making the invisible visible” (2000a: 309). Too much information may  blind us from seeing more clearly and, ultimately, from understanding (Strathern, 2000a; Tsoukas, 1997). Unknowing does not foreclose knowledge but challenges it. In this sense, the kind of unknowing I have in mind can be likened to Bataille’s notion of “nonknowledge” as “a form of excess that challenges both our thinking and our ethics” (Yusof, 2009: 1014). For Bataille, nonknowledge is not something that must be eradicated but embraced as an enriching experience (1986, 2004).7 Baudrillard develops the distinction between “obscenity” and “seduction” in response to Bataille’s thinking about the knowledge/nonknowledge divide, stating how “nonknowledge is the seductive and magical aspect of knowledge” ( Juergenson & Rey, 2012: 290). In other words, we might think of “unknowing algorithms” as a form of distancing or a form of engaging with the seductive qualities of algorithms that cannot always be explained in fully rational terms. On a practical level, unknowing algorithms may simply imply opening the black box of one’s own assumptions about knowing algorithms. For a computer scientist, this may imply knowing more about the social dynamics impinging on the data that algorithms are designed to process. Conversely, for social scientists and humanities scholars, it might imply knowing more about how computers and algorithms make decisions (Kroll et al.,  2016: 14). On a more theoretical and conceptual level, unknowing algorithms implies confronting the limits of the metaphor of the black box itself. Grappling with what Karin Knorr-Cetina calls negative knowledge, the task is  to identify the limits and imperfections of the black box metaphor. For Knorr-Cetina:\n\n46\n\nIF . . . THEN","previous_page":45,"next_page":47}