Applying the Stereotype Content Model to assess disability bias in popular pre-trained NLP models underlying AI-based assistive technologies

Abstract

Stereotypes are a positive or negative, generalized, and often widely shared belief about the attributes of certain groups of people, such as people with sensory disabilities. If stereotypes manifest in assistive technologies used by deaf or blind people, they can harm the user in a number of ways, especially considering the vulnerable nature of the target population. AI models underlying assistive technologies have been shown to contain biased stereotypes, including racial, gender, and disability biases. We build on this work to present a psychology-based stereotype assessment of the representation of disability, deafness, and blindness in BERT using the Stereotype Content Model. We show that BERT contains disability bias, and that this bias differs along established stereotype dimensions.