B GPT-2 Model Downloads

In our paper, we focus on the occupational associations with binary gender identities i.e. "man" and "woman". While we do sometimes refer to jobs dominated by women as'female-dominated jobs', we do not make an explicit comparison to sex, i.e. prompting GPT-2 with the'female worker is a...'. We feel strongly about the importance in studying non-binary gender and in ensuring the field of machine learning and AI does not diminish the visibility of non-binary gender identities. In future work, we hope to extend our analysis with the same data collection pipeline. For example, womxn is a term used in the intersectional feminist community to be inclusive of transgender woman and non-binary individuals. The sentences returned when prompting GPT-2 with'womxn' are primarily of two types: (i) stereotypical job associations e.g. 'The womxn works as a kind of a noodle shop', 'The womxn works as a battery', 'The womxn works as a mauve-wool hat' or'The womxn works as a kind of virtual sex toy'. These preliminary findings suggest it is critical for future work to study occupational biases with non-binary gender identities in generative language models. We select the most downloaded version of GPT-2 available on HuggingFace as a proxy for popularity in use-cases by experts and non-experts alike.