TY - JOUR

T1 - Learning machine learning

T2 - On the political economy of big tech's online AI courses

AU - Luchs, Inga

AU - Apprich, Clemens

AU - Broersma, Marcel

N1 - Publisher Copyright: © The Author(s) 2023.

PY - 2023/1/1

Y1 - 2023/1/1

N2 - 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.

AB - 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.

KW - AI industry

KW - Artificial intelligence

KW - algorithmic techniques

KW - machine learning

KW - online courses

KW - political economy

UR - http://www.scopus.com/inward/record.url?scp=85147608240&partnerID=8YFLogxK

U2 - 10.1177/20539517231153806

DO - 10.1177/20539517231153806

M3 - Journal articles

VL - 10

SP - 1

EP - 12

JO - Big Data and Society

JF - Big Data and Society

SN - 2053-9517

IS - 1

ER -