{"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":90,"pdf_page":100,"physical_index":100,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"2103f9f159bb59102c248b809d673559141ef0322141d09d1ed775cee5296b22"},"chapter":"IF...THEN","content":"Conclusion\n\nGiven the 1.28 billion active daily Facebook users, algorithmic practices, techniques, and procedures play a powerful role in deciding what gets shown, to whom, and in what ways. Algorithms are never neutral but reflect the values and cultural assumptions of the people who write them. This does not mean that we can simply understand algorithms by asking programmers. While ethnographic work exploring how algorithms are created may tell us something about what goes into the ways an algorithm functions, programmers are no more equipped than other people to talk explicitly about their biases, values, or cultural assumptions. Instead, this chap ter reveals how an understanding of the power and politics of algorithms can be approached by considering their sociomateriality. Guided by the question of how the news feed works, for what possible purpose, I argued for a critical, architectural reading of algorithms. Through a close reading of publicly available documents de scribing the workings of the Facebook news feed as well as systematic observations and analysis of my own news feed, several aspects were revealed about the ways in which sociality and subjectivity are imaged by the platform and how it is systemati cally embedded and engineered into algorithmic arrangements and mechanics. Despite the emphasis on the material politics of algorithms in this chapter, material ity is never decoupled or distinct from the domain of the social. With much recent focus on the algorithm as an object of analysis within the social sciences and humanities, there has been a certain tendency to imbue too much causal power to the algorithm as if it were a single, easily discernible thing. What has become apparent is that there is an urgent need to historicize algorithms, to put algorithms into broader contexts of production and consumption. At the same time, cultural theorists have to grapple with the inevitable ephemerality of social media algorithms. In this chapter, I have tried to engage with this historicity by considering the cultural beliefs and values held by creators of software and by being explicit about the dates and times of data capture. This is to say that we need to acknowledge that the news feed and its algorithm(s) emerge from somewhere; evolve in particular ways; are talked about in certain terms; mystified; reflect certain beliefs and assumptions; work to accomplish certain technical, economic, and cultural goals; stabilize temporarily, then change again; and so on. It also means that tapping into the algorithmic assemblage will only ever be a tapping into a momentary tracing and stabilisation of a series of events.\n\nIndeed, the analysis of the news feed algorithms presented in this chapter reveals the “variable ontology” characteristic of software (Mackenzie,  2006). While it might be a stretch to claim that Mark Zuckerberg’s love of gray T-shirts is an algorithmic metaphor, the piece of clothing does reveal something about the values and beliefs he holds. In spite of restricted access to Facebook engineers, we still find plenty of traces of how platforms operate, how their CEOs and managers think and talk, how code is made and work organized, the ways in which culture maters, how culture gets branded, and the values, norms, and cultural assumptions held by the people involved. These traces include but are not exclusive to the principle of moving furniture, the notion that the physical layout of Facebook’s headquarters is designed to change the thinking of Facebook employees, the technical specifications described in patent documents, the engineering rhetoric perpetuated at developer conferences, media reports, and user interface. The study shows how algorithms are ontogenetic simply because the problems that require a solution continuously change. While the problem of showing the most “interesting” content remains, what constitutes interestingness or relevance depends on the given context. Facebook is never finished. Whether we are talking about the physical refurbishing of sofas or the news feed ranking algorithm, the object of study is inherently dynamic, changing, and mutable. In an age of machine learning in which the models used to predict user behavior constantly change as a result of increasing user data, it may prove particularly challenging to capture the underlying logic of how the system works. If, as Foucault suggested, disciplinary power is efectuated by means of training subjects to reach their full potentiality as useful individuals, what we see emerging now is a certain disciplining of the algorithm by means of training the machine to learn correctly from a corpus of known data. What we are still only at the beginning of understanding, let alone addressing in the first place, is what “correct training” means and which techniques are put into place to ensure that algorithms reach their full potentiality as useful machines.\n\n90\n\nI F . . . T H E N","previous_page":89,"next_page":91}