  It is evident that deep text classification models trained on human data
could be biased. In particular, they produce biased outcomes for texts that
explicitly include identity terms of certain demographic groups. We refer to
this type of bias as explicit bias, which has been extensively studied.
However, deep text classification models can also produce biased outcomes for
texts written by authors of certain demographic groups. We refer to such bias
as implicit bias of which we still have a rather limited understanding. In this
paper, we first demonstrate that implicit bias exists in different text
classification tasks for different demographic groups. Then, we build a
learning-based interpretation method to deepen our knowledge of implicit bias.
Specifically, we verify that classifiers learn to make predictions based on
language features that are related to the demographic attributes of the
authors. Next, we propose a framework Debiased-TC to train deep text
classifiers to make predictions on the right features and consequently mitigate
implicit bias. We conduct extensive experiments on three real-world datasets.
The results show that the text classification models trained under our proposed
framework outperform traditional models significantly in terms of fairness, and
also slightly in terms of classification performance.
