{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:43:23Z","timestamp":1777455803921,"version":"3.51.4"},"reference-count":76,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Big Data &amp; Society"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>Machine learning (ML) algorithms are still a novel research object in the field of media studies. While existing research focuses on concrete software on the one hand and the socio-economic context of the development and use of these systems on the other, this paper studies online ML courses as a research object that has received little attention so far. By pursuing a walkthrough and critical discourse analysis of Google's Machine Learning Crash Course and IBM's introductory course to Machine Learning with Python, we not only shed light on the technical knowledge, assumptions, and dominant infrastructures of ML as a field of practice, but also on the economic interests of the companies providing the courses. We demonstrate how the online courses further support Google and IBM to consolidate and even expand their position of power by recruiting new AI talent and by securing their infrastructures and models to become the dominant ones. Further, we show how the companies not only influence greatly how ML is represented, but also how these representations in turn influence and direct current ML research and development, as well as the societal effects of their products. Here, they boast an image of fair and democratic artificial intelligence, which stands in stark contrast to the ubiquity of their corporate products and the advertised directives of efficiency and performativity the companies strive for. This underlines the need for alternative infrastructures and perspectives.<\/jats:p>","DOI":"10.1177\/20539517231153806","type":"journal-article","created":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T00:59:12Z","timestamp":1675817952000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":17,"title":["Learning machine learning: On the political economy of big tech's online AI courses"],"prefix":"10.1177","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2731-0549","authenticated-orcid":false,"given":"Inga","family":"Luchs","sequence":"first","affiliation":[{"name":"Centre for Media and Journalism Studies, University of Groningen, Groningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7619-3801","authenticated-orcid":false,"given":"Clemens","family":"Apprich","sequence":"additional","affiliation":[{"name":"Department of Media Theory, University of Applied Arts Vienna, Wien, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7342-3472","authenticated-orcid":false,"given":"Marcel","family":"Broersma","sequence":"additional","affiliation":[{"name":"Centre for Media and Journalism Studies, University of Groningen, Groningen, The Netherlands"}]}],"member":"179","published-online":{"date-parts":[[2023,2,7]]},"reference":[{"key":"bibr1-20539517231153806","volume-title":"Cloud Ethics. 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Available at: https:\/\/www.wired.com\/2008\/06\/pb-theory\/ (accessed 17 August 2021)."},{"key":"bibr3-20539517231153806","doi-asserted-by":"publisher","DOI":"10.14361\/dcs-2018-0104"},{"key":"bibr4-20539517231153806","volume-title":"Pattern Discrimination","author":"Apprich C","year":"2019"},{"issue":"3","key":"bibr5-20539517231153806","first-page":"671","volume":"104","author":"Barocas S","year":"2016","journal-title":"Californian Law Review"},{"key":"bibr6-20539517231153806","doi-asserted-by":"publisher","DOI":"10.1177\/2053951718819569"},{"key":"bibr7-20539517231153806","doi-asserted-by":"publisher","DOI":"10.17265\/2159-5313\/2016.09.003"},{"key":"bibr8-20539517231153806","doi-asserted-by":"publisher","DOI":"10.1080\/1369118X.2012.678878"},{"key":"bibr9-20539517231153806","doi-asserted-by":"publisher","DOI":"10.1177\/2053951720983865"},{"key":"bibr10-20539517231153806","volume-title":"If\u2026\u2009Then. 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Available at: https:\/\/www.wired.com\/2015\/11\/google-open-sources-its-artificial-intelligence-engine\/ (accessed 17 August 2021)."},{"key":"bibr51-20539517231153806","volume-title":"Machine Learning","author":"Mitchell TM","year":"1997"},{"key":"bibr52-20539517231153806","doi-asserted-by":"publisher","DOI":"10.18574\/nyu\/9781479833641.001.0001"},{"key":"bibr53-20539517231153806","volume-title":"Weapons of Math Destruction","author":"O'Neil C","year":"2016"},{"key":"bibr54-20539517231153806","doi-asserted-by":"publisher","DOI":"10.4159\/harvard.9780674736061"},{"key":"bibr55-20539517231153806","doi-asserted-by":"publisher","DOI":"10.1177\/2053951720939605"},{"key":"bibr56-20539517231153806","unstructured":"Pichai S (2017) Making AI Work for Everyone.\n                      Google Blog\n                      , 17 May. 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Paper presented to\n                      Data and Discrimination: Converting Critical Concerns into Productive Inquiry\n                      , May 22."},{"key":"bibr66-20539517231153806","unstructured":"Seaver N (2013) Knowing Algorithms. Paper presented at\n                      Media in Transition 8, Cambridge, MA\n                      . Unpublished Draft. Available at: http:\/\/nickseaver.net\/papers\/seaverMiT8.pdf (accessed 17 August 2021)."},{"key":"bibr67-20539517231153806","doi-asserted-by":"publisher","DOI":"10.1177\/1359183518820366"},{"key":"bibr68-20539517231153806","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287598"},{"key":"bibr69-20539517231153806","unstructured":"Simonite T (2021) A Second AI Researcher Says She Was Fired by Google.\n                      WIRED\n                      , 19 February. 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An Outsider\u2019s Journey into Tech Culture, Programming, and Mathemagics","author":"Svensson J","year":"2021"},{"key":"bibr73-20539517231153806","unstructured":"TensorFlow (2021)\n                      Official Website.\n                      Available at: https:\/\/www.tensorflow.org\/ (accessed 17 August 2021)."},{"key":"bibr74-20539517231153806","unstructured":"West SM, Whittaker M, Crawford K (2019) Discriminating Systems. Gender, Race, and Power in AI.\n                      AI Now Institute.\n                      Available at: https:\/\/ainowinstitute.org\/ discriminatingsystems.html (accessed 17 August 2021)."},{"key":"bibr75-20539517231153806","unstructured":"Wiley C (2021) Google Cloud Unveils Vertex AI, One Platform, Every ML Tool You Need.\n                      Google Cloud Blog\n                      , 18 May. 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