Measuring Misogyny in Natural Language Generation: Preliminary Results
  from a Case Study on two Reddit Communities
Aaron J. Snoswell Lucinda Nelson Hao Xue Flora D. Salim Nicolas Suzor Jean Burgess
Generic `toxicity' classifiers continue to be used for evaluating the
potential for harm in natural language generation, despite mounting evidence of
their shortcomings. We consider the challenge of measuring misogyny in natural
language generation, and argue that generic `toxicity' classifiers are
inadequate for this task. We use data from two well-characterised `Incel'
communities on Reddit that differ primarily in their degrees of misogyny to
construct a pair of training corpora which we use to fine-tune two language
models. We show that an open source `toxicity' classifier is unable to
distinguish meaningfully between generations from these models. We contrast
this with a misogyny-specific lexicon recently proposed by feminist
subject-matter experts, demonstrating that, despite the limitations of simple
lexicon-based approaches, this shows promise as a benchmark to evaluate
language models for misogyny, and that it is sensitive enough to reveal the
known differences in these Reddit communities. Our preliminary findings
highlight the limitations of a generic approach to evaluating harms, and
further emphasise the need for careful benchmark design and selection in
natural language evaluation.