Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting

TLDR

Researchers have identified unintended biases in text classification datasets, where certain demographic identity terms are disproportionately flagged as abusive. The study aims to formalize these unintended biases as a selection bias and develop a model‑agnostic debiasing framework. The framework recovers the non‑discrimination distribution via instance weighting based on a predefined set of demographic identity terms, requiring no additional annotations. Experiments show the method effectively reduces unintended bias while preserving model generalization.

Abstract

With the recent proliferation of the use of text classifications, researchers have found that there are certain unintended biases in text classification datasets. For example, texts containing some demographic identity-terms (e.g., “gay”, “black”) are more likely to be abusive in existing abusive language detection datasets. As a result, models trained with these datasets may consider sentences like “She makes me happy to be gay” as abusive simply because of the word “gay.” In this paper, we formalize the unintended biases in text classification datasets as a kind of selection bias from the non-discrimination distribution to the discrimination distribution. Based on this formalization, we further propose a model-agnostic debiasing training framework by recovering the non-discrimination distribution using instance weighting, which does not require any extra resources or annotations apart from a pre-defined set of demographic identity-terms. Experiments demonstrate that our method can effectively alleviate the impacts of the unintended biases without significantly hurting models’ generalization ability.