Abstract
Advances in machine learning and natural language processing are revolutionizing the way we live, work, and think. As for any science, they are based on assumptions about what the world is, and how humans interact with it. In this paper, I discuss what is potentially one of these assumptions: structuralism, which states that all cultures share a hidden structure. I illustrate this assumption with political footprints: a machine-learning technique using pre-trained word vectors for political discourse analysis. I introduce some of the benefits and limitations of structuralism when applied to machine learning, and the risks of exploiting a technology before establishing the validity of all its hypotheses. I consider how machine-learning techniques could evolve towards hybrid structuralism or post-structuralism, and how deeply these developments would impact cultural studies.
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Acknowledgement
I would like to thank Niel Chah (Chah, n.d.) for his support during the political footprint development process, Araz Taeihagh (Taeihagh, n.d.) and the reviewers from AI and Society for their constructive feedback.
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Bruchansky, C. Machine learning: A structuralist discipline?. AI & Soc 34, 931–938 (2019). https://doi.org/10.1007/s00146-017-0764-x
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DOI: https://doi.org/10.1007/s00146-017-0764-x