Reducing Gender Bias in Abusive Language Detection
Open accessAbstract
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.
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Toxicity classifiers can pick up on biases in the training data and spuriously associate toxicity with the mention of certain demographics.
“Prior work has shown that toxicity classifiers can pick up on biases in the training data and spuriously associate toxicity with the mention of certain demographics (Park et al., 2018; Dixon et al., 2018).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Park et al., 2018
Models trained with historical data can present issues such as gender bias and racial bias, potentially exacerbating structural inequalities.
“models are trained with historical data and can present issues such as gender bias and racial bias in AI models [68,73], potentially exacerbating structural inequalities [10]”
From Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation (2022) · cites this paper as [68]
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Steed, R., Panda, S., Kobren, A., & Wick, M. J. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.247 · Cited by 42 · see citing works
A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spaces
Lauscher, A., Glavaš, G., Ponzetto, S. P., & Vulić, I. (2020) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v34i05.6325 · Cited by 42 · see citing works
Sentiment Analysis Is Not Solved! Assessing and Probing Sentiment Classification
Barnes, J., Øvrelid, L., & Velldal, E. (2019) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/w19-4802 · Cited by 42 · see citing works
Towards multidomain and multilingual abusive language detection: a survey
Pamungkas, E. W., Basile, V., & Patti, V. (2021) · Personal and Ubiquitous Computing · doi:10.1007/s00779-021-01609-1 · Cited by 40 · see citing works
A taxonomy and review of generalization research in NLPPreprint
Hupkes, D., Giulianelli, M., Dankers, V., Artetxe, M., Elazar, Y., Pimentel, T., Christodoulopoulos, C., Lasri, K., Saphra, N., Sinclair, A., et al. (2022) · arXiv (Cornell University) · doi:10.3929/ethz-b-000638992 · Cited by 41 · see citing works
ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Detection in Conversational AI
Curry, A. C., Abercrombie, G., & Rieser, V. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.587 · Cited by 50 · see citing works
Recognizing Euphemisms and Dysphemisms Using Sentiment Analysis
Felt, C., & Riloff, E. (2020) · Workshop on Figurative Language Processing (FigLang) · doi:10.18653/v1/2020.figlang-1.20 · Cited by 38 · see citing works
Reducing sentiment polarity for demographic attributes in word embeddings using adversarial learning
Sweeney, C., & Najafian, M. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3372837 · Cited by 38 · see citing works
Large Language Model Supply Chain: A Research Agenda
Wang, S., Zhao, Y., Hou, X., & Wang, H. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3708531 · Cited by 47 · see citing works
A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis
Hosseini-Asl, E., Liu, W., & Xiong, C. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.58 · Cited by 42 · see citing works
A Causal Inference Method for Reducing Gender Bias in Word Embedding Relations
Yang, Z., & Feng, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6486 · Cited by 35 · see citing works
Hullman, J. R., Kapoor, S., Nanayakkara, P., Gelman, A., & Narayanan, A. (2022) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3514094.3534196 · Cited by 37 · see citing works
Cyberbullying Detection With Fairness Constraints
Gencoglu, O. (2021) · IEEE Internet Computing · doi:10.1109/mic.2020.3032461 · Cited by 53 · see citing works
FIND: Human-in-the-Loop Debugging Deep Text Classifiers
Lertvittayakumjorn, P., Specia, L., & Toni, F. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.24 · Cited by 35 · see citing works
Hassan, S., Huenerfauth, M., & Alm, C. O. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.267 · Cited by 43 · see citing works
Balayn, A., Yang, J., Szlávik, Z., & Bozzon, A. (2021) · ACM Transactions on Social Computing · doi:10.1145/3479158 · Cited by 33 · see citing works
Mitigating Gender Bias in Natural Language Processing: Literature ReviewPreprint
Sun, T., Gaut, A., Tang, S., Huang, Y., ElSherief, M., Zhao, J., Mirza, D., Belding, E., Chang, K.-W., & Wang, W. Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.08976 · Cited by 109 · see citing works
HateCheck: Functional Tests for Hate Speech Detection Models
Röttger, P., Vidgen, B., Nguyen, D., Waseem, Z., Margetts, H. Z., & Pierrehumbert, J. B. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.4 · Cited by 235 · see citing works
Gender bias in transformers: A comprehensive review of detection and mitigation strategies
Nemani, P., Joel, Y. D., Vijay, P., & Liza, F. F. (2023) · Natural Language Processing Journal · doi:10.1016/j.nlp.2023.100047 · Cited by 33 · see citing works
Ball-Burack, A., Lee, M. S. A., Cobbe, J., & Singh, J. P. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445875 · Cited by 32 · see citing works
Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis
Liu, X., Yin, D., Feng, Y., Wu, Y., & Zhao, D. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.155 · Cited by 39 · see citing works
Gender Bias in Contextualized Word EmbeddingsPreprint
Zhao, J., Wang, T., Yatskar, M., Cotterell, R., Ordóñez, V., & Chang, K.-W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.03310 · Cited by 71 · see citing works
Shortcut Learning Explanations for Deep Natural Language Processing: A Survey on Dataset Biases
Dogra, V., Verma, S., Kavita, K., Woźniak, M., Shafi, J., & Ijaz, M. F. (2024) · IEEE Access · doi:10.1109/access.2024.3360306 · Cited by 34 · see citing works
Benning, L., Peintner, A., & Peintner, L. (2022) · Cancers · doi:10.3390/cancers14030623 · Cited by 32 · see citing works
Evaluating GPT-3 Generated Explanations for Hateful Content Moderation
Wang, H., Hee, M. S., Awal, M. R., Choo, K. T. W., & Lee, R. K.-W. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/694 · Cited by 32 · see citing works
Mitigating Bias in Session-based Cyberbullying Detection: A Non-Compromising Approach
Cheng, L., Mosallanezhad, A., Silva, Y. N., Hall, D. L., & Liu, H. (2021) · doi:10.18653/v1/2021.acl-long.168 · Cited by 31 · see citing works
- Authors
- Park, J. H., Shin, J., & Fung, P.
- Affiliations
- Hong Kong University of Science and Technology (HK)
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Published
- 2018
- Pages
- 2799-2804
- OpenAlex
- W2888167352
- Cited by
- 323
- References
- 13
- Citations by year
- 2026: 1 · 2025: 23 · 2024: 26 · 2023: 56 · 2022: 53 · 2021: 75 · 2020: 53 · 2019: 34 · 2018: 2
- FWCI
- 16.75
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Hong Kong University of Science and Technology
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- offensive language detection, gender bias, model bias, bias mitigation, fine-tuning, imbalanced training data, word embeddings
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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