Machine Learning as a Model for Cultural Learning: Teaching an Algorithm What it Means to be Fat

Alina Arseniev-Koehler and Jacob G. Foster

Sociological Methods & Research, 2022, vol. 51, issue 4, 1484-1539

Abstract: Public culture is a powerful source of cognitive socialization; for example, media language is full of meanings about body weight. Yet it remains unclear how individuals process meanings in public culture. We suggest that schema learning is a core mechanism by which public culture becomes personal culture. We propose that a burgeoning approach in computational text analysis – neural word embeddings – can be interpreted as a formal model for cultural learning. Embeddings allow us to empirically model schema learning and activation from natural language data. We illustrate our approach by extracting four lower-order schemas from news articles: the gender, moral, health, and class meanings of body weight. Using these lower-order schemas we quantify how words about body weight “fill in the blanks†about gender, morality, health, and class. Our findings reinforce ongoing concerns that machine-learning models (e.g., of natural language) can encode and reproduce harmful human biases.

Keywords: word embeddings; schema; culture; meaning; cognition (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:51:y:2022:i:4:p:1484-1539

DOI: 10.1177/00491241221122603

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