{"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":154,"pdf_page":164,"physical_index":164,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"6430367b280a0d6ca276bb336fca1cf03a2ec3eb248f218f9ea4e0fcd1d56e88"},"chapter":"IF...THEN","content":"This also means that the roles of designers, developers, and other decision-makers are crucial. Just because algorithms learn and amplify existing societal biases, inequalities or stereotypes, it does not mean that they cannot be corrected for. On the contrary, algorithm developers can compensate for the bias in datasets, and companies do make choices about when to intervene in correcting certain algorithmic outcomes.3 When algorithms end up as biased toward certain groups, these instances can be identified and handled computationally. As was discussed in chapter 6, using algorithms in journalism does not automatically entail the creation of filter bubbles, contrary to what is often claimed. As one of the interviewees suggested, “If you want your algorithm to reflect diversity that’s what you have to design for.” What’s important is how designers, developers and decision makers think about what algorithms should be optimized for, and what possible consequences this may have for diferent social groups.\n\nOrientations\n\nAgency is not all or nothing. To say “the algorithm did it” will not do. Nor is it an option to assign responsibility to others, just because there are always other entities involved. As with the controversies surrounding algorithms discussed in the book, it is not a question whether algorithms conceived of as non-human objects or humans did this or that, but how the atribution of agency is realized in particular setings. Yet, the question of “how exactly to be responsible, how to hold or to be held accountable if agency is distributed?” (Simon, 2015: 153) remains. As crucial as this question is, distributed agency does not make accountability impossible. On the contrary, the agential messiness entailed by such a relational view makes accountability mandatory (Barad, 1998). There are at least two aspects to this. First, agency is not necessarily equally or symmetrically distributed. Secondly, the process of making certain elements more or less available or unavailable to specific actors is not something that occurs naturally, but rather enacted through boundary-making practices. What does this entail for an understanding algorithmic power and politics?\n\n154\n\nI F . . . T H E N","previous_page":153,"next_page":155}