HateDebias: On the Diversity and Variability of Hate Speech Debiasing

2024-06-07

arXiv.org

AI Summary

Existing hate speech detection datasets overlook the diversity and temporal evolution of bias types and distributions, failing to reflect real-world dynamic bias environments. To address this, we introduce HateDebias—the first debiasing benchmark explicitly designed for dynamic bias scenarios—featuring multi-source, multi-type, and temporally evolving bias distributions, enabling robust evaluation under continual learning. Methodologically, we present the first systematic modeling of bias diversity and variability, proposing a continual debiasing framework that jointly integrates bias-informed regularization and memory replay. Extensive experiments on HateDebias demonstrate that our approach significantly improves accuracy across mainstream models and effectively mitigates performance degradation across shifting bias domains. These results validate its effectiveness and generalizability in realistic social media environments.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Hate speech on social media is ubiquitous but urgently controlled. Without detecting and mitigating the biases brought by hate speech, different types of ethical problems. While a number of datasets have been proposed to address the problem of hate speech detection, these datasets seldom consider the diversity and variability of bias, making it far from real-world scenarios. To fill this gap, we propose a benchmark, named HateDebias, to analyze the model ability of hate speech detection under continuous, changing environments. Specifically, to meet the diversity of biases, we collect existing hate speech detection datasets with different types of biases. To further meet the variability (i.e., the changing of bias attributes in datasets), we reorganize datasets to follow the continuous learning setting. We evaluate the detection accuracy of models trained on the datasets with a single type of bias with the performance on the HateDebias, where a significant performance drop is observed. To provide a potential direction for debiasing, we further propose a debiasing framework based on continuous learning and bias information regularization, as well as the memory replay strategies to ensure the debiasing ability of the model. Experiment results on the proposed benchmark show that the aforementioned method can improve several baselines with a distinguished margin, highlighting its effectiveness in real-world applications.

Problem

• Addressing bias diversity and variability in hate speech detection
• Evaluating model fairness in dynamically evolving social media environments
• Mitigating performance degradation from specific bias attributes in datasets

Innovation

• Constructed HateDebias benchmark for dynamic bias analysis
• Integrated memory replay with bias regularization techniques
• Collected diverse real-world hate speech datasets