Source-linked AI summary
Reducing Gender Bias in Abusive Language Detection
Ji Ho Park, Jamin Shin, Pascale Fung
TL;DR
Abusive-language classifiers can associate neutral identity terms with abuse, making bias measurement important for practical robustness. The paper evaluates gender bias across datasets, embeddings, and architectures, then tests three mitigation methods. The methods reduce gender bias by 90–98%, with reported performance trade-offs.
Problem
Abusive-language models may overfit identity words and exhibit unintended gender bias, while biased evaluation sets make that bias difficult to measure.
Method
The paper measures gender bias with an unbiased generated test set, compares embeddings and architectures, and evaluates debiased embeddings, gender-swap augmentation, and larger-corpus fine-tuning.
Results
Gender-bias reduction reached 90–98%; combining debiased embeddings with gender swapping reduced equality differences by 98% and 89% while losing 1.5% of original performance.
Takeaways & Limitations
Measuring and correcting gender bias can improve model robustness beyond conventional performance evaluation, and the proposed methods may extend to other identity problems and tasks.
Takeaways & Limitations
The work is preliminary and focuses on gender terms, although the authors state that the methods may extend to racial identities and other tasks.
Abstract
from arXiv · showhide
Abusive language detection models tend to have a problem of being biased toward identity words of a certain group of people because of imbalanced training datasets. For example, "You are a good woman" was considered "sexist" when trained on an existing dataset. Such model bias is an obstacle for models to be robust enough for practical use. In this work, we measure gender biases on models trained with different abusive language datasets, while analyzing the effect of different pre-trained word embeddings and model architectures. We also experiment with three bias mitigation methods: (1) debiased word embeddings, (2) gender swap data augmentation, and (3) fine-tuning with a larger corpus. These methods can effectively reduce gender bias by 90-98% and can be extended to correct model bias in other scenarios.
1 Introduction
The paper examines unintended gender bias in abusive-language classifiers, which can mistake neutral sentences for sexist content and undermine practical generalization. It measures this bias and evaluates three reduction methods alongside embeddings and architectures.
- Abusive-language detection matters because online abuse can contribute to trauma, cyber-bullying, hate crime, and discrimination.
- Existing classifiers can exhibit unfair biases toward particular groups, raising concerns about the robustness of abusive-language detection systems.
- Supervised models may overfit frequent neutral identity words, causing sentences such as “You are a good woman” to be classified as sexist.
- False positive bias is defined as unintended performance differences across comments containing different identity terms.
- The paper measures gender bias with an unbiased test set and proposes debiased embeddings, gender-swapped augmentation, and larger-corpus fine-tuning.
2 Related Work
Prior work developed abusive-language datasets and classifiers, while newer NLP research examined bias and methods for measuring or correcting gender stereotypes. Abusive-language systems had not explicitly addressed model bias in the cited work.
- Abusive-language datasets have been constructed from multiple sources using crowd-sourcing or user moderation.
- Deep-learning approaches have trained automatic abusive-language classifiers on these datasets, but the cited studies did not explicitly address model bias.
- NLP bias research includes correcting gender stereotypes in word embeddings and measuring associations with the Word Embedding Association Test.
3 Datasets
The paper uses sexist-tweet and large crowdsourced abusive-tweet datasets as sources for studying gender bias in abusive-language detection.
- Sexist Tweets (st): The sexist-tweet dataset contains Twitter posts retrieved with sexism-related terms and annotated by experts using criteria founded in critical race theory.
- Sexist Tweets (st): The study retains sexist samples from the original dataset to focus on gender biases.
- Abusive Tweets: Founta et al. released a crowdsourced dataset containing 60K abusive tweets.
- Abusive Tweets: The crowdsourced dataset identifies None, Spam, Abusive, and Hateful as relevant labels, with reported proportions of 11% Abusive, 7.5% Hateful, and 22.5% Spam.
4 Measuring Gender Biases
Gender bias is measured on separately generated, balanced male–female test sets using equality-difference fairness metrics alongside AUC. Experiments compare embeddings and neural architectures, showing that embeddings can improve task performance while increasing bias scores.
- Test-set construction: Because original test sets inherit dataset bias, the paper generates separate unbiased male and female test sets using identity-term templates.
- Test-set construction: Template pairs differ only in identity terms, so matched sentences should receive the same abusive-language prediction.
- Test-set construction: The generated evaluation contains 1,152 samples organized as 576 gender-swapped pairs, with neutral and offensive vocabulary.
- Metrics: The evaluation reports AUC on original and generated test sets plus FPED and FNED, which measure differences between overall and gender-specific error rates.
- Metrics: Equality-difference scores assess fairness, complementing AUC measures of model performance and generalization.
- Experimental setup: The experiments compare CNN, GRU, and bidirectional GRU with self-attention architectures.
- Results: Pre-trained embeddings improved task and generated-test ROC performance, while equality-difference scores tended to be larger with pre-trained embeddings.
Gen. AUC
The abt dataset yields lower false negative and false positive equality differences than the st dataset, while generated-test performance is evaluated with these fairness measures.
- The abt dataset has significantly smaller false negative and false positive equality differences than the st dataset.
- The models’ generated-test results include false positive equality difference scores for the st dataset.
5 Reducing Gender Biases
The study experiments with several methods to reduce gender bias identified in abusive-language detection models.
- The authors experiment with various methods to reduce gender biases identified in their models.
Gen. AUC
Three mitigation methods are evaluated on the st dataset. Gender swapping substantially reduces equality differences, while combined debiased embeddings and gender swapping achieve the strongest reported reduction with limited original-performance loss.
- The experiments evaluate debiased embeddings, gender-swap augmentation, and bias fine-tuning using a larger, less-biased source corpus.
- Gender swapping significantly reduces both false positive and false negative equality differences.
- Fine-tuning with a larger, less-biased source dataset decreases equality differences and greatly improves AUC on the generated unbiased test set.
- 98% and 89% equality-difference reductions result from combining debiased embeddings and gender swapping on GRU, with only 1.5% original-performance loss.
- Applying all three methods improves generated-unbiased-set performance and equality differences but produces the largest decrease in original performance.
- All mitigation methods incur some performance loss, with fine-tuning showing the largest decrease in original test-set performance in these experiments.
6 Conclusion & Future Work
The study finds that gender bias is influenced by embeddings, architectures, and datasets, and that the proposed methods reduce bias substantially while improving robustness. Future work targets performance-preserving mitigation and broader identity and task settings.
- Pre-trained word embeddings, model architecture, and dataset choice all influence gender bias in abusive-language detection.
- The proposed methods reduce gender bias by up to 90-98% and improve model robustness.
- Future work could develop mitigation methods that reduce bias without causing classification-performance drops, including adversarial training for latent language variables.
- Although preliminary and focused on gender terms, the work describes extensions to racial identity problems and sentiment analysis.