  Fairness and environmental impact are important research directions for the
sustainable development of artificial intelligence. However, while each topic
is an active research area in natural language processing (NLP), there is a
surprising lack of research on the interplay between the two fields. This
lacuna is highly problematic, since there is increasing evidence that an
exclusive focus on fairness can actually hinder environmental sustainability,
and vice versa. In this work, we shed light on this crucial intersection in NLP
by (1) investigating the efficiency of current fairness approaches through
surveying example methods for reducing unfair stereotypical bias from the
literature, and (2) evaluating a common technique to reduce energy consumption
(and thus environmental impact) of English NLP models, knowledge distillation
(KD), for its impact on fairness. In this case study, we evaluate the effect of
important KD factors, including layer and dimensionality reduction, with
respect to: (a) performance on the distillation task (natural language
inference and semantic similarity prediction), and (b) multiple measures and
dimensions of stereotypical bias (e.g., gender bias measured via the Word
Embedding Association Test). Our results lead us to clarify current assumptions
regarding the effect of KD on unfair bias: contrary to other findings, we show
that KD can actually decrease model fairness.
