Moreover, even when such pretrained models are available, they inevitably come with potentially undesirable biases baked in, based on the data on which they were trained (Recasens et al., 2013;Bolukbasi et al., 2016;Zhao et al., 2019).
for social science applications, it may be preferable to exclude such confounders by only working with in-domain or curated data.