As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
This paper explores a methodology for bias quantification in transformer-based deep neural network language models for Chinese, English, and French. When queried with health-related mythbusters on COVID-19, we observe a bias that is not of a semantic/encyclopaedical knowledge nature, but rather a syntactic one, as predicted by theoretical insights of structural complexity. Our results highlight the need for the creation of health-communication corpora as training sets for deep learning.
loading subjects...
