  Hate speech detection is a common downstream application of natural language
processing (NLP) in the real world. In spite of the increasing accuracy,
current data-driven approaches could easily learn biases from the imbalanced
data distributions originating from humans. The deployment of biased models
could further enhance the existing social biases. But unlike handling tabular
data, defining and mitigating biases in text classifiers, which deal with
unstructured data, are more challenging. A popular solution for improving
machine learning fairness in NLP is to conduct the debiasing process with a
list of potentially discriminated words given by human annotators. In addition
to suffering from the risks of overlooking the biased terms, exhaustively
identifying bias with human annotators are unsustainable since discrimination
is variable among different datasets and may evolve over time. To this end, we
propose an automatic misuse detector (MiD) relying on an explanation method for
detecting potential bias. And built upon that, an end-to-end debiasing
framework with the proposed staged correction is designed for text classifiers
without any external resources required.
