Machine learning as a model for cultural learning: Teaching an algorithm what it means to be fat
This addresses the problem of understanding cultural bias transmission for researchers in social sciences and AI ethics, though it is incremental as it applies an existing method to new data.
The paper tackled how cultural biases, such as those related to body weight, are learned from public culture, using neural word embeddings to extract schemata from New York Times articles that link obesity to gender, immorality, poor health, and low socioeconomic class.
As we navigate our cultural environment, we learn cultural biases, like those around gender, social class, health, and body weight. It is unclear, however, exactly how public culture becomes private culture. In this paper, we provide a theoretical account of such cultural learning. We propose that neural word embeddings provide a parsimonious and cognitively plausible model of the representations learned from natural language. Using neural word embeddings, we extract cultural schemata about body weight from New York Times articles. We identify several cultural schemata that link obesity to gender, immorality, poor health, and low socioeconomic class. Such schemata may be subtly but pervasively activated in public culture; thus, language can chronically reproduce biases. Our findings reinforce ongoing concerns that machine learning can also encode, and reproduce, harmful human biases.