Machine Learning as a Model for Cultural Learning: Teaching an Algorithm What It Means to Be Fat

Autor/inn/en	Arseniev-Koehler, Alina; Foster, Jacob G.
Titel	Machine Learning as a Model for Cultural Learning: Teaching an Algorithm What It Means to Be Fat
Quelle	In: Sociological Methods & Research, 51 (2022) 4, S.1484-1539 (56 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Arseniev-Koehler, Alina) ORCID (Foster, Jacob G.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0049-1241
DOI	10.1177/00491241221122603
Schlagwörter	Artificial Intelligence; Cultural Influences; Body Weight; Gender Differences; Moral Values; Health; Social Class; Social Bias; Schemata (Cognition); Obesity; Socialization; Socioeconomic Status; Vocabulary + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Cultural influence; Kultureinfluss; Körpergewicht; Geschlechterkonflikt; Moral value; Ethischer Wert; Gesundheit; Social classes; Soziale Klasse; Cognition; Schema; Kognition; Adipositas; Socialisation; Sozialisation; Socio-economic status; Sozioökonomischer Status; Wortschatz
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01