  Natural Language Processing (NLP) models have been found discriminative
against groups of different social identities such as gender and race. With the
negative consequences of these undesired biases, researchers have responded
with unprecedented effort and proposed promising approaches for bias
mitigation. In spite of considerable practical importance, current algorithmic
fairness literature lacks an in-depth understanding of the relations between
different forms of biases. Social bias is complex by nature. Numerous studies
in social psychology identify the "generalized prejudice", i.e., generalized
devaluing sentiments across different groups. For example, people who devalue
ethnic minorities are also likely to devalue women and gays. Therefore, this
work aims to provide a first systematic study toward understanding bias
correlations in mitigation. In particular, we examine bias mitigation in two
common NLP tasks -- toxicity detection and word embeddings -- on three social
identities, i.e., race, gender, and religion. Our findings suggest that biases
are correlated and present scenarios in which independent debiasing approaches
dominant in current literature may be insufficient. We further investigate
whether jointly mitigating correlated biases is more desired than independent
and individual debiasing. Lastly, we shed light on the inherent issue of
debiasing-accuracy trade-off in bias mitigation. This study serves to motivate
future research on joint bias mitigation that accounts for correlated biases.
