Machine learning as a model for cultural learning: Teaching an algorithm what it means to be fat

Overweight individuals, and especially women, are disparaged as immoral, unhealthy, and low class. These negative conceptions are not intrinsic to obesity; they are the tainted fruit of cultural learning. Scholars often cite media consumption as a key mechanism for learning cultural biases, but it remains unclear how this public culture becomes private culture. Here we provide a computational account of this learning mechanism, showing that cultural schemata can be learned from news reporting. We extract schemata about obesity from New York Times articles with word2vec, a neural language model inspired by human cognition. 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 by our language; thus, language can chronically reproduce biases (e.g., about weight and health). Our findings also reinforce ongoing concerns that machine learning can encode, and reproduce, harmful human biases.