An investigation of structures responsible for gender bias in BERT and
  DistilBERT
Thibaud Leteno Antoine Gourru Charlotte Laclau Christophe Gravier
In recent years, large Transformer-based Pre-trained Language Models (PLM)
have changed the Natural Language Processing (NLP) landscape, by pushing the
performance boundaries of the state-of-the-art on a wide variety of tasks.
However, this performance gain goes along with an increase in complexity, and
as a result, the size of such models (up to billions of parameters) represents
a constraint for their deployment on embedded devices or short-inference time
tasks. To cope with this situation, compressed models emerged (e.g.
DistilBERT), democratizing their usage in a growing number of applications that
impact our daily lives. A crucial issue is the fairness of the predictions made
by both PLMs and their distilled counterparts. In this paper, we propose an
empirical exploration of this problem by formalizing two questions: (1) Can we
identify the neural mechanism(s) responsible for gender bias in BERT (and by
extension DistilBERT)? (2) Does distillation tend to accentuate or mitigate
gender bias (e.g. is DistilBERT more prone to gender bias than its uncompressed
version, BERT)? Our findings are the following: (I) one cannot identify a
specific layer that produces bias; (II) every attention head uniformly encodes
bias; except in the context of underrepresented classes with a high imbalance
of the sensitive attribute; (III) this subset of heads is different as we
re-fine tune the network; (IV) bias is more homogeneously produced by the heads
in the distilled model.