{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"a2eab101-adfc-4a0d-8142-63a4d11cee02","title":"人工智能和资本主义的未来","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9780745338613","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"a2eab101-adfc-4a0d-8142-63a4d11cee02","id":"a2eab101-adfc-4a0d-8142-63a4d11cee02","uuid":"a2eab101-adfc-4a0d-8142-63a4d11cee02","title":"人工智能和资本主义的未来","normalized_title":"人工智能和资本主义的未来","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9780745338613","isbns":["9780745338613","9780745338606","9781786803955","9781786803979","9781786803962"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":225,"navigation_url":"https://workshop.closeclaw.top/ai/book/a2eab101-adfc-4a0d-8142-63a4d11cee02","title_url":"https://workshop.closeclaw.top/ai/title/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E5%92%8C%E8%B5%84%E6%9C%AC%E4%B8%BB%E4%B9%89%E7%9A%84%E6%9C%AA%E6%9D%A5","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/a2eab101-adfc-4a0d-8142-63a4d11cee02/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/a2eab101-adfc-4a0d-8142-63a4d11cee02/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/a2eab101-adfc-4a0d-8142-63a4d11cee02","page_url":"https://workshop.closeclaw.top/api/ai/books/a2eab101-adfc-4a0d-8142-63a4d11cee02/page","search_url":"https://workshop.closeclaw.top/api/ai/books/a2eab101-adfc-4a0d-8142-63a4d11cee02/search"},"page":{"printed_page":77,"pdf_page":85,"physical_index":85,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"64d2839068a092b29ed2c79c4fcb4701f89b517e273ecc318375203fc738dd1c"},"chapter":"Digital Barricades: Interventions in Digital Culture and Politics","content":"Much of this ‘data munging’ or ‘data wrangling’ (Johnston 2015) is outsourced, often as offshored microwork, in low-wage zones from Venezuela to India and Indonesia, at rates ranging from a few dollars to a few cents an hour (Nakashima 2018a). At first this was handled through crowdsourcing platforms such as Freelancer, Mechanical Turk or Upwork. Then data cleaning became a specialized business. Start-ups like Mighty AI and Figure Eight (formerly CrowdFlower) develop software that ‘makes it easier to label photos and other data, even on smartphones’ (Nakashima 2018a). Playment, a Bangalore company, ‘gamifies’ such tasks and calls its 30,000 on-call workers ‘players’. In China, firms specializing in ‘refined data processing’ work with a Taylorist division of labour. Some ‘project groups’ are responsible for labelling ‘nodes on the human body in complex yoga-like postures’, or annotating ‘motorcycles, bicycles and pedestrians’ showing their travel directions for autonomous vehicle software; others ‘scan the contours of buildings or obscured objects, used to train radar to detect real-world objects’ (Yu 2017). The work is demanding and tiring.\n\nThe other layer of the ML ‘sandwich’, deployment and monitoring, also draws on global clickwork. ML algorithms may not work as anticipated, or encounter problems requiring intervention. How much purportedly automated systems depend on human backstops was highlighted in 2017, when Expensify, a company offering to automatically scan photos of receipts to extract data for expense reports was discovered to have bridged failures in its text analysis software by sending customers’ personally identifiable receipts to workers on Amazon’s Mechanical Turk (MTurk) crowdsourcing platform. This kind of ‘human safety net’ behind AI-powered services is very common (Gent 2017). Sarah Roberts (2016, 2017) has identified the role of generally poorly paid and precarious human content moderators in filtering social media feeds to purge violent and pornographic content. Google is reported to have an army of 10,000 ‘raters’ watching YouTube videos. Microsoft operates a Universal Human Relevance System, checking the results of search algorithms with millions of micro-tasks a month. Facebook and Google remediation of ML outcomes was recently intensified by ‘cyber-war’ scandals (Alba 2017; Dyer-Witheford and Matviyenko 2019). The microwork of data cleaning and monitoring are closely related; both may be performed by the same low-paid contract home-worker on different shifts (Nakashima 2018c).\n\nautomating the social factory\n\n77","previous_page":76,"next_page":78}