"I'm sorry to hear that": finding bias in language models with a holistic descriptor dataset. (arXiv:2205.09209v1 [cs.CL])

As language models grow in popularity, their biases across all possible
markers of demographic identity should be measured and addressed in order to
avoid perpetuating existing societal harms. Many datasets for measuring bias
currently exist, but they are restricted in their coverage of demographic axes,
and are commonly used with preset bias tests that presuppose which types of
biases the models exhibit. In this work, we present a new, more inclusive
dataset, HOLISTICBIAS, which consists of nearly 600 descriptor terms …

arxiv bias dataset language language models