  Various existing studies have analyzed what social biases are inherited by
NLP models. These biases may directly or indirectly harm people, therefore
previous studies have focused only on human attributes. However, until recently
no research on social biases in NLP regarding nonhumans existed. In this paper,
we analyze biases to nonhuman animals, i.e. speciesist bias, inherent in
English Masked Language Models such as BERT. We analyzed speciesist bias
against 46 animal names using template-based and corpus-extracted sentences
containing speciesist (or non-speciesist) language. We found that pre-trained
masked language models tend to associate harmful words with nonhuman animals
and have a bias toward using speciesist language for some nonhuman animal
names. Our code for reproducing the experiments will be made available on
GitHub.
