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

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 c...

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Bibliographic Details
Published in:Sociological methods & research 2022-11, Vol.51 (4), p.1484-1539
Main Authors: Arseniev-Koehler, Alina, Foster, Jacob G.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Body Weight
Cultural Influences
Culture
Gender
Gender Differences
Health
Human body
Learning
Machine learning
Meaning
Moral Values
Morality
News
Obesity
Schemas
Schemata (Cognition)
Social Bias
Social Class
Socialization
Socioeconomic Status
Teaching
Vocabulary
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Summary: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.
ISSN:0049-1241
1552-8294
DOI:10.1177/00491241221122603