HateDebias：论仇恨言论去偏的多样性与变异性
HateDebias: On the Diversity and Variability of Hate Speech Debiasing
AI总结：
针对现有仇恨言论数据集未考虑偏见多样性与变异性的问题，提出基准HateDebias及整合记忆重放与偏见信息正则化的持续去偏框架，提升模型在动态场景下的公平性与实用性。
摘要：
仇恨言论在社交媒体平台上频繁出现，亟需得到有效管控。缓解仇恨言论引发的偏见有助于解决各类伦理问题。尽管现有研究已构建了若干仇恨言论检测数据集，但这些数据集很少考虑偏见的多样性与变异性，使其与真实场景相去甚远。为填补这一空白，我们提出基准HateDebias，以分析模型在动态演化环境下的公平性。具体而言，为满足偏见的多样性，我们从真实场景中收集包含不同类型偏见的仇恨言论数据；为进一步模拟真实场景中的变异性（即数据集中偏见属性的变化），我们构建遵循持续学习设置的数据集，并在HateDebias上评估模型的检测精度——性能下降表明模型对特定属性存在显著偏见。为提供潜在解决方向，我们进一步提出针对真实场景动态偏见的持续去偏框架，整合记忆重放与偏见信息正则化以确保模型公平性。在HateDebias基准上的实验结果显示，我们的方法在缓解真实场景动态偏见方面性能有所提升，凸显其在实际应用中的实用性。
英文摘要：
Hate speech frequently appears on social media platforms and urgently needs to be effectively controlled. Alleviating the bias caused by hate speech can help resolve various ethical issues. Although existing research has constructed several datasets for hate speech detection, these datasets seldom consider the diversity and variability of bias, making them far from real-world scenarios. To fill this gap, we propose a benchmark HateDebias to analyze the fairness of models under dynamically evolving environments. Specifically, to meet the diversity of biases, we collect hate speech data with different types of biases from real-world scenarios. To further simulate the variability in the real-world scenarios(i.e., the changing of bias attributes in datasets), we construct a dataset to follow the continuous learning setting and evaluate the detection accuracy of models on the HateDebias, where performance degradation indicates a significant bias toward a specific attribute. To provide a potential direction, we further propose a continual debiasing framework tailored to dynamic bias in real-world scenarios, integrating memory replay and bias information regularization to ensure the fairness of the model. Experiment results on the HateDebias benchmark reveal that our methods achieve improved performance in mitigating dynamic biases in real-world scenarios, highlighting the practicality in real-world applications.
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