  Language carries implicit human biases, functioning both as a reflection and
a perpetuation of stereotypes that people carry with them. Recently, ML-based
NLP methods such as word embeddings have been shown to learn such language
biases with striking accuracy. This capability of word embeddings has been
successfully exploited as a tool to quantify and study human biases. However,
previous studies only consider a predefined set of biased concepts to attest
(e.g., whether gender is more or less associated with particular jobs), or just
discover biased words without helping to understand their meaning at the
conceptual level. As such, these approaches can be either unable to find biased
concepts that have not been defined in advance, or the biases they find are
difficult to interpret and study. This could make existing approaches
unsuitable to discover and interpret biases in online communities, as such
communities may carry different biases than those in mainstream culture. This
paper improves upon, extends, and evaluates our previous data-driven method to
automatically discover and help interpret biased concepts encoded in word
embeddings. We apply this approach to study the biased concepts present in the
language used in online communities and experimentally show the validity and
stability of our method

Computation and Language;Artificial Intelligence;Computers and Society