  In efforts to keep up with the rapid progress and use of large language
models, gender bias research is becoming more prevalent in NLP. Non-English
bias research, however, is still in its infancy with most work focusing on
English. In our work, we study how grammatical gender bias relating to
politeness levels manifests in Japanese and Korean language models. Linguistic
studies in these languages have identified a connection between gender bias and
politeness levels, however it is not yet known if language models reproduce
these biases. We analyze relative prediction probabilities of the male and
female grammatical genders using templates and find that informal polite speech
is most indicative of the female grammatical gender, while rude and formal
speech is most indicative of the male grammatical gender. Further, we find
politeness levels to be an attack vector for allocational gender bias in
cyberbullying detection models. Cyberbullies can evade detection through simple
techniques abusing politeness levels. We introduce an attack dataset to (i)
identify representational gender bias across politeness levels, (ii)
demonstrate how gender biases can be abused to bypass cyberbullying detection
models and (iii) show that allocational biases can be mitigated via training on
our proposed dataset. Through our findings we highlight the importance of bias
research moving beyond its current English-centrism.
