{"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":56,"pdf_page":66,"physical_index":66,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"faa2225ef410cf39c4f0cba8e8b9056e31e6061caa2cd2d413c693774084a3eb"},"chapter":"IF...THEN","content":"From the perspective of relational materialism, the questions that mater the most are “not philosophical in character, but political” (Mol, 2013: 381). Returning to the notion of ontological politics introduced at the end of the previous chapter, engaging with the question of how algorithms come to mater in contemporary society is not about trying to define what they are or at what points they act but, rather, about questioning the ways in which they are enacted and come together to make diferent versions of reality. I think what the many contrasting examples of controversies involving the algorithm-human continuum show is how algorithms are not inherently good or bad, neutral or biased, but are made to appear in one way or the other, depending on a whole range of diferent factors, interests, stakeholders, strategies and, indeed, politics. The term “ontological politics” is precisely meant to highlight how realities are never given but shaped and emerge through interactions.22 Instead of determining who acts (or discriminates, for that mater), the interesting question is what and when an actor becomes in the particular ways in which the entity is active (Passoth et al., 2012: 4).\n\nBy shifting atention away from asking what and where agency is to when agency is and to whom agency belongs in specific situations, we may begin to see how the notion of algorithms as black boxes may not just be an ontological and epistemological claim but, ultimately, a political one as well. As I said in the beginning of this chapter, positioning something as a black box serves diferent functions. The black box of algorithm is not simply an unknown but, in many cases, constitutes what Linsey McGoey (2012) has called a strategic unknown, understood as the strategic harnessing of ignorance. As McGoey asserts, strategic unknowns highlight the ways in which “cultivating ignorance is often more advantageous, both institutionally and personally, than cultivating knowledge” (2012: 555). In the case of disaster management, for example, experts’ claims of ignorance soften any alleged responsibility for the disaster or scandal in question. By mobilizing unknowns strategically, organizations and individuals can insist that detection or prior knowledge was impossible. These forms of ignorance are frequently mobilized in discourses on algorithms and software as well.\n\nIn fact, it seems that machine learning and the field of artificial intelligence often appear as entire fields of strategic unknowns. At the most fundamental level, the operations of machine-learning algorithms seem to preclude any form of certainty. Because the machine learns “on its own” without being explicitly programmed to do so, there is no way of knowing what exactly caused a certain outcome. As Dourish reflects on the advent of machine learning. “During my years of computer science training, to have an algorithm was to know something. Algorithms were definitive procedures that lead to predictable results. The outcome of the algorithmic operation was known and certain” (2016: 7). Machine-learning techniques, on the other hand, produce unknowns:\n\n56\n\nI F . . . T H E N","previous_page":55,"next_page":57}