We are working on studying methods to detect gendered language automatically using unsupervised learning methods, such as variational auto-encoders. The findings of our first paper on this (Hoyle et al., 2019) have been reported by 75+ international news outlets, including Forbes.
Currently, we are interested in expanding the above to a cross-lingual study, as well as researching the relationship between gender bias and attitudes towards entities on social media as part of a project funded by DFF.
Moreover, in a Carlsberg-funded project starting in autumn 2023, we will be investigating fair and accountable Natural Language Processing methods, which can be used to understand what influences the employer images that organisations project in job ads.
