With the resurgence of recurrent models and their applicability in capturing temporal information in sequences, there is a risk of finding latent spaces that are not properly being fairly distributed. This can be of higher risk in unsupervised models that operate in data that is not properly curated. If you think about it, the model does not care about the relationships that it finds; it only cares about minimizing a loss function, and therefore if it is trained with magazines or newspapers from the 1950s, it may find spaces where the word "women" may be close (in terms of Euclidean distance) to home labor words such as "broom", "dishes", and "cooking", while the word "man" may be close to all other labor such as "driving", "teaching", "doctor", and "scientist". This is an example of a bias that has been introduced into the latent space (Shin, S., et al. (2020)).
The risk here...
