# Stereotypical Bias Removal for Hate Speech Detection Task using Knowledge-based Generalizations

arXiv: https://arxiv.org/abs/2001.05495 · HTML: https://mlsanity.com/paper/2001.05495/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2020.

## Built on by (most cited first, 30 of 40)

- [Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics](https://mlsanity.com/paper/2106.14574/) (2021)
- [Towards generalisable hate speech detection: a review on obstacles and solutions](https://mlsanity.com/paper/2102.08886/) (2021)
- [Handling Bias in Toxic Speech Detection: A Survey](https://mlsanity.com/paper/2202.00126/) (2022)
- [Representation Bias in Data: A Survey on Identification and Resolution Techniques](https://mlsanity.com/paper/2203.11852/) (2022)
- [Balancing out Bias: Achieving Fairness Through Balanced Training](https://mlsanity.com/paper/2109.08253/) (2021)
- [Contrastive Learning for Fair Representations](https://mlsanity.com/paper/2109.10645/) (2021)
- [On Explaining Multimodal Hateful Meme Detection Models](https://mlsanity.com/paper/2204.01734/) (2022)
- [Fairness-aware Class Imbalanced Learning](https://mlsanity.com/paper/2109.10444/) (2021)
- [Controlling Bias Exposure for Fair Interpretable Predictions](https://mlsanity.com/paper/2210.07455/) (2022)
- [Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors](https://mlsanity.com/paper/2204.02261/) (2022)
- [StereoKG: Data-Driven Knowledge Graph Construction for Cultural Knowledge and Stereotypes](https://mlsanity.com/paper/2205.14036/) (2022)
- [The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias](https://mlsanity.com/paper/2312.16148/) (2023)
- [Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLP](https://mlsanity.com/paper/2302.05711/) (2023)
- [An Information Retrieval Approach to Building Datasets for Hate Speech Detection](https://mlsanity.com/paper/2106.09775/) (2021)
- [Towards Equal Opportunity Fairness through Adversarial Learning](https://mlsanity.com/paper/2203.06317/) (2022)
- [Mitigating Biases in Toxic Language Detection through Invariant Rationalization](https://mlsanity.com/paper/2106.07240/) (2021)
- [Reinforcement Guided Multi-Task Learning Framework for Low-Resource Stereotype Detection](https://mlsanity.com/paper/2203.14349/) (2022)
- [InterFair: Debiasing with Natural Language Feedback for Fair Interpretable Predictions](https://mlsanity.com/paper/2210.07440/) (2022)
- [Hate Speech Detection with Generalizable Target-aware Fairness](https://mlsanity.com/paper/2406.00046/) (2024)
- [Unsupervised Domain Adaptation for Hate Speech Detection Using a Data Augmentation Approach](https://mlsanity.com/paper/2107.12866/) (2021)
- [Hidden behind the obvious: misleading keywords and implicitly abusive language on social media](https://mlsanity.com/paper/2205.01374/) (2022)
- [Measuring Geographic Performance Disparities of Offensive Language Classifiers](https://mlsanity.com/paper/2209.07353/) (2022)
- [Unintended Bias in Language Model-driven Conversational Recommendation](https://mlsanity.com/paper/2201.06224/) (2022)
- [fairlib: A Unified Framework for Assessing and Improving Classification Fairness](https://mlsanity.com/paper/2205.01876/) (2022)
- [A Comprehensive Study of Gender Bias in Chemical Named Entity Recognition Models](https://mlsanity.com/paper/2212.12799/) (2022)
- [SWE2: SubWord Enriched and Significant Word Emphasized Framework for Hate Speech Detection](https://mlsanity.com/paper/2409.16673/) (2024)
- [A Neighbourhood Framework for Resource-Lean Content Flagging](https://mlsanity.com/paper/2103.17055/) (2021)
- [Beyond Detection: Unveiling Fairness Vulnerabilities in Abusive Language Models](https://mlsanity.com/paper/2311.09428/) (2023)
- [Algorithmic Fairness: A Tolerance Perspective](https://mlsanity.com/paper/2405.09543/) (2024)
- [KTCR: Improving Implicit Hate Detection with Knowledge Transfer driven Concept Refinement](https://mlsanity.com/paper/2410.15314/) (2024)

## Abstract (arXiv)

With the ever-increasing cases of hate spread on social media platforms, it is critical to design abuse detection mechanisms to proactively avoid and control such incidents. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. Bias removal has been traditionally studied for structured datasets, but we aim at bias mitigation from unstructured text data. In this paper, we make two important contributions. First, we systematically design methods to quantify the bias for any model and propose algorithms for identifying the set of words which the model stereotypes. Second, we propose novel methods leveraging knowledge-based generalizations for bias-free learning. Knowledge-based generalization provides an effective way to encode knowledge because the abstraction they provide not only generalizes content but also facilitates retraction of information from the hate speech detection classifier, thereby reducing the imbalance. We experiment with multiple knowledge generalization policies and analyze their effect on general performance and in mitigating bias. Our experiments with two real-world datasets, a Wikipedia Talk Pages dataset (WikiDetox) of size ~96k and a Twitter dataset of size ~24k, show that the use of knowledge-based generalizations results in better performance by forcing the classifier to learn from generalized content. Our methods utilize existing knowledge-bases and can easily be extended to other tasks