Power of Explanations: Towards automatic debiasing in hate speech detection

TL;DR

This paper introduces an automatic bias detection and correction framework for hate speech detection models, leveraging explanation methods to identify and mitigate biases without external resources, improving fairness in NLP classifiers.

Contribution

The paper proposes a novel automatic bias detection method using explanation techniques and an end-to-end debiasing framework for text classifiers, eliminating reliance on human-annotated biased terms.

Findings

Effective bias detection without external resources

Improved fairness in hate speech detection models

Framework adaptable to evolving biases

Abstract

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.