Measuring and Mitigating Unintended Bias in Text Classification
Open accessAbstract
We introduce and illustrate a new approach to measuring and mitigating unintended bias in machine learning models. Our definition of unintended bias is parameterized by a test set and a subset of input features. We illustrate how this can be used to evaluate text classifiers using a synthetic test set and a public corpus of comments annotated for toxicity from Wikipedia Talk pages. We also demonstrate how imbalances in training data can lead to unintended bias in the resulting models, and therefore potentially unfair applications. We use a set of common demographic identity terms as the subset of input features on which we measure bias. This technique permits analysis in the common scenario where demographic information on authors and readers is unavailable, so that bias mitigation must focus on the content of the text itself. The mitigation method we introduce is an unsupervised approach based on balancing the training dataset. We demonstrate that this approach reduces the unintended bias without compromising overall model quality.
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Real data often has disproportionate amounts of toxicity directed at specific groups.
“Synthetic data is valuable here because [11] shows that real data often has disproportionate amounts of toxicity directed at specific groups.”
From Model Cards for Model Reporting (2019) · cites this paper as [11]
The 'Quantifying bias in dataset' section (page unavailable) reports that certain identity terms appear disproportionately in toxic comments in the authors’ training data; for example, 'gay' appears in 3% of toxic comments but 0.5% of comments overall. It measures comments containing those terms, not whether toxicity is directed at the groups, and does not establish how often this pattern occurs across real datasets generally. (LLM, conf=0.85)
Experimental — accuracy not yet measuredToxicity 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 Dixon et al., 2018
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Cites
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879 · see citing works
Equality of Opportunity in Supervised Learning
Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536 · see citing works
Certifying and Removing Disparate Impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783311 · Cited by 1,827 · see citing works
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016) · arXiv · doi:10.48550/arxiv.1607.06520 · Cited by 1,615 · see citing works
Inherent Trade-Offs in the Fair Determination of Risk Scores
Kleinberg, J., Mullainathan, S., & Raghavan, M. (2016) · arXiv · doi:10.48550/arxiv.1609.05807 · Cited by 941 · see citing works
Ex Machina: Personal Attacks Seen at Scale
Wulczyn, E., Thain, N., & Dixon, L. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052591 · Cited by 636 · see citing works
Self-citationInherent Tradeoffs in the Fair Determination of Risk Scores
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603197 · Cited by 347 · see citing works
Gender and Dialect Bias in YouTube’s Automatic Captions
Tatman, R. (2017) · Proceedings of the First ACL Workshop on Ethics in Natural Language Processing · doi:10.18653/v1/w17-1606 · Cited by 338 · see citing works
The social impact of natural language processing
Hovy, D., & Spruit, S. L. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p16-2096 · Cited by 329 · see citing works
Data Decisions and Theoretical Implications when Adversarially Learning Fair RepresentationsPreprint
Beutel, A., Chen, J., Zhao, Z., & Chi, E. H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.00075 · Cited by 282 · see citing works
On the (im)possibility of fairness
Friedler, S. A., Scheidegger, C., & Venkatasubramanian, S. (2016) · arXiv · doi:10.48550/arxiv.1609.07236 · Cited by 247 · see citing works
Blodgett, S. L., & O'Connor, B. (2017) · arXiv · doi:10.48550/arxiv.1707.00061 · Cited by 153 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Model Cards for Model Reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681 · see citing works
“A model card could even extend beyond these summary statistics to reveal other measures of differences between distributions such as cross entropy, perplexity, KL divergence and pinned area under the curve (pinned AUC) [13].”
BackgroundMethodInfluentialSelf-citationPaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590 · see citing works
“We note that we are subject to the social bias of the Perspective API, which can assign higher toxicity to innocuous mentions of certain identity groups (Dixon et al., 2018; Röttger et al., 2021).”
BackgroundInfluentialLanguage (Technology) is Power: A Critical Survey of “Bias” in NLP
Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.485 · Cited by 1,050 · see citing works
“A.2 Full categorization: Motivations Allocational harms Hovy and Spruit (2016); Caliskan et al. (2017); Madnani et al. (2017); Dixon et al. (2018); Kiritchenko and Mohammad (2018); Shen et al. (2018); Zhao et al. (2018b); Bhaskaran and Bhallamudi (2019); Bordia and Bowman (2019); Brunet et al.…”
BackgroundInfluentialThe Risk of Racial Bias in Hate Speech Detection
Sap, M., Card, D., Gabriel, S., Choi, Y., & Smith, N. A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1163 · Cited by 817 · see citing works
Taxonomy of Risks posed by Language Models
Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533088 · Cited by 764 · see citing works
RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models
Gehman, S., Gururangan, S., Sap, M., Choi, Y., & Smith, N. A. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.301 · Cited by 693 · see citing works
Bias and Fairness in Large Language Models: A Survey
Gallegos, I. O., Rossi, R. A., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., & Ahmed, N. K. (2024) · Computational Linguistics · doi:10.1162/coli_a_00524 · Cited by 665 · see citing works
Fairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646 · see citing works
HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection
Mathew, B., Saha, P., Yimam, S. M., Biemann, C., Goyal, P., & Mukherjee, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i17.17745 · Cited by 585 · see citing works
Data and its (dis)contents: A survey of dataset development and use in machine learning research
Paullada, A., Raji, I. D., Bender, E. M., Denton, E., & Hanna, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100336 · Cited by 548 · see citing works
“Dixon et al. [2018] found that a dataset for toxicity classification contained a disproportionate association between words describing queer identities and text labeled as ‘toxic.’”
BackgroundWILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518 · see citing works
“Unfortunately, prior work has documented biases in automatic moderation tools; for example, toxicity classifiers have been shown to spuriously associate the mention of certain demographic groups with toxicity (Park et al., 2018; Dixon et al., 2018).”
Mitigating Gender Bias in Natural Language Processing: Literature Review
Sun, T., Gaut, A., Tang, S., Huang, Y., ElSherief, M., Zhao, J., Mirza, D., Belding, E., Chang, K.-W., & Wang, W. Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1159 · Cited by 492 · see citing works
Racial Bias in Hate Speech and Abusive Language Detection Datasets
Davidson, T., Bhattacharya, D., & Weber, I. (2019) · Proceedings of the Third Workshop on Abusive Language Online · doi:10.18653/v1/w19-3504 · Cited by 414 · see citing works
Fairness in Recommendation Ranking through Pairwise Comparisons
Beutel, A., Chen, J., Doshi, T. S., Qian, H., Wei, L., Wu, Y., Heldt, L., Zhao, Z., Hong, L., Ed, H., et al. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330745 · Cited by 374 · see citing works
Red Teaming Language Models with Language Models
Perez, E., Huang, S., Song, F., Cai, T., Ring, R., Aslanides, J., Glaese, A., McAleese, N., & Irving, G. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.225 · Cited by 363 · see citing works
The hateful memes challenge: Detecting hate speech in multimodal memes
Kiela, D., Firooz, H., Mohan, A., Goswami, V., Singh, A., Ringshia, P., & Testuggine, D. (2020) · arXiv · doi:10.48550/arxiv.2005.04790 · Cited by 352 · see citing works
Learning the Difference that Makes a Difference with Counterfactually-Augmented DataPreprint
Kaushik, D., Hovy, E. H., & Lipton, Z. C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.12434 · Cited by 346 · see citing works
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting
De-Arteaga, M., Romanov, A., Wallach, H., Chayes, J., Borgs, C., Chouldechova, A., Geyik, S., Kenthapadi, K., & Kalai, A. T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287572 · Cited by 337 · see citing works
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
Borkan, D., Dixon, L., Sorensen, J. S., Thain, N., & Vasserman, L. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308560.3317593 · Cited by 335 · see citing works
Self-citationReducing Gender Bias in Abusive Language Detection
Park, J. H., Shin, J., & Fung, P. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1302 · Cited by 323 · see citing works
Five sources of bias in natural language processing
Hovy, D., & Prabhumoye, S. (2021) · Language and Linguistics Compass · doi:10.1111/lnc3.12432 · Cited by 298 · see citing works
Should ChatGPT be biased? Challenges and risks of bias in large language models
Ferrara, E. (2023) · First Monday · doi:10.5210/fm.v28i11.13346 · Cited by 282 · see citing works
Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models
Ferrara, E. (2023) · arXiv · doi:10.48550/arxiv.2304.03738 · Cited by 282
ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection
Hartvigsen, T., Gabriel, S., Palangi, H., Sap, M., Ray, D., & Kamar, E. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.234 · Cited by 279 · see citing works
Social Biases in NLP Models as Barriers for Persons with Disabilities
Hutchinson, B., Prabhakaran, V., Denton, E., Webster, K., Zhong, Y., & Denuyl, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.487 · Cited by 274 · see citing works
Evaluating Models’ Local Decision Boundaries via Contrast Sets
Gardner, M., Artzi, Y., Basmov, V., Berant, J., Bogin, B., Chen, S., Dasigi, P., Dua, D., Elazar, Y., Gottumukkala, A., et al. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.117 · Cited by 265 · see citing works
Trustworthy ai: A computational perspective
Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., Liu, Y., Jain, A., & Tang, J. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3546872 · Cited by 261 · see citing works
Chatbots: Security, privacy, data protection, and social aspects
Hasal, M., Nowaková, J., Ahmed Saghair, K., Abdulla, H., Snášel, V., & Ogiela, L. (2021) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.6426 · Cited by 249 · see citing works
Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology
Zmigrod, R., Mielke, S. J., Wallach, H., & Cotterell, R. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1161 · Cited by 241 · see citing works
Bots and Misinformation Spread on Social Media: Implications for COVID-19
Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M. M., Ungar, L., Schwartz, H. A., Epstein, D. H., Leggio, L., & Curtis, B. L. (2021) · Journal of Medical Internet Research · doi:10.2196/26933 · Cited by 238 · see citing works
Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations
Davani, A. M., Díaz, M., & Prabhakaran, V. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00449 · Cited by 237 · 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
GeDi: Generative Discriminator Guided Sequence Generation
Krause, B., Gotmare, A. D., McCann, B., Keskar, N. S., Joty, S., Socher, R., & Rajani, N. F. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.424 · Cited by 220 · see citing works
Towards generalisable hate speech detection: a review on obstacles and solutions
Yin, W., & Zubiaga, A. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.598 · Cited by 220 · see citing works
Challenges and frontiers in abusive content detection
Vidgen, B., Harris, A., Nguyen, D., Tromble, R., Hale, S. A., & Margetts, H. Z. (2019) · Workshop on Abusive Language Online (ALW) · doi:10.18653/v1/w19-3509 · Cited by 206 · see citing works
Hate speech detection and racial bias mitigation in social media based on BERT model
Mozafari, M., Farahbakhsh, R., & Crespi, N. (2020) · PLOS ONE · doi:10.1371/journal.pone.0237861 · Cited by 260 · see citing works
Latent Hatred: A Benchmark for Understanding Implicit Hate Speech
ElSherief, M., Ziems, C., Muchlinski, D., Anupindi, V., Seybolt, J., Choudhury, M. D., & Yang, D. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.29 · Cited by 201 · see citing works
Counterfactual Fairness in Text Classification through Robustness
Garg, S., Perot, V., Limtiaco, N., Taly, A., Ed, H., & Beutel, A. (2019) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3306618.3317950 · Cited by 200 · see citing works
DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts
Liu, A., Sap, M., Lu, X., Swayamdipta, S., Bhagavatula, C., Smith, N. A., & Choi, Y. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.522 · Cited by 199 · see citing works
Transforming Conversations with AI—A Comprehensive Study of ChatGPT
Bansal, G., Chamola, V., Hussain, A., Guizani, M., & Niyato, D. T. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10236-2 · Cited by 190 · see citing works
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection
Vidgen, B., Thrush, T., Waseem, Z., & Kiela, D. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.132 · Cited by 174 · see citing works
Learning The Difference That Makes A Difference With Counterfactually-Augmented Data
Kaushik, D., Hovy, E. H., & Lipton, Z. C. (2020) · International Conference on Learning Representations · Cited by 174 · see citing works
“Kelly is a Warm Person, Joseph is a Role Model”: Gender Biases in LLM-Generated Reference Letters
Wan, Y., Pu, G., Sun, J., Garimella, A., Chang, K.-W., & Peng, N. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.243 · Cited by 175 · see citing works
Singhal, A., Neveditsin, N., Tanveer, H., & Mago, V. (2024) · JMIR Medical Informatics · doi:10.2196/50048 · Cited by 172 · see citing works
Algorithmic Censorship by Social Platforms: Power and Resistance
Cobbe, J. (2020) · Philosophy & Technology · doi:10.1007/s13347-020-00429-0 · Cited by 161 · see citing works
SoK: Hate, Harassment, and the Changing Landscape of Online Abuse
Thomas, K., Akhawe, D., Bailey, M., Boneh, D., Bursztein, E., Consolvo, S., Dell, N. L., Durumeric, Z., Kelley, P. G., Kumar, D., et al. (2021) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp40001.2021.00028 · Cited by 159 · see citing works
A New Generation of Perspective API: Efficient Multilingual Character-level Transformers
Lees, A., Tran, V. Q., Tay, Y., Sorensen, J., Gupta, J., Metzler, D., & Vasserman, L. (2022) · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3534678.3539147 · Cited by 155 · see citing works
Self-citation"I wouldn't say offensive but...": Disability-Centered Perspectives on Large Language Models
Gadiraju, V., Kane, S., Dev, S., Taylor, A., Wang, D., Denton, R., & Brewer, R. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3593989 · Cited by 155 · see citing works
Representation Bias in Data: A Survey on Identification and Resolution Techniques
Shahbazi, N., Yin, L., Asudeh, A., & Jagadish, H. V. (2023) · ACM Computing Surveys · doi:10.1145/3588433 · Cited by 155 · see citing works
Feng, S., Park, C. Y., Liu, Y., & Tsvetkov, Y. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.656 · Cited by 149 · see citing works
Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era
Dai, S., Chen, X., Xu, S., Pang, L., Dong, Z., & Xu, J. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671458 · Cited by 148 · see citing works
Explainable AI: current status and future directionsPreprint
Gohel, P., Singh, P., & Mohanty, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.07045 · Cited by 148 · see citing works
Detection of Abusive Language: the Problem of Biased Datasets
Wiegand, M., Ruppenhofer, J., & Kleinbauer, T. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1060 · Cited by 145 · see citing works
Bogina, V., Hartman, A., Kuflik, T., & Shulner-Tal, A. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00248-0 · Cited by 142 · see citing works
Towards trustworthy LLMs: a review on debiasing and dehallucinating in large language models
Lin, Z., Guan, S., Zhang, W., Zhang, H., Li, Y., & Zhang, H. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10896-y · Cited by 141 · see citing works
Contextualizing Hate Speech Classifiers with Post-hoc Explanation
Kennedy, B., Jin, X., Davani, A. M., Dehghani, M., & Ren, X. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.483 · Cited by 133 · see citing works
Recipes for Safety in Open-domain ChatbotsPreprint
Xu, J., Ju, D., Li, M., Boureau, Y.-L., Weston, J., & Dinan, E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.07079 · Cited by 130 · see citing works
Fairness in Machine Learning: A Survey
Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646 · see citing works
Algorithmic fairness datasets: the story so far
Fabris, A., Messina, S., Silvello, G., & Susto, G. A. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00854-z · Cited by 128 · see citing works
Unified Concept Editing in Diffusion Models
Gandikota, R., Orgad, H., Belinkov, Y., Materzyńska, J., & Bau, D. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00503 · Cited by 127 · see citing works
Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?
Miceli, M., Posada, J., & Yang, T. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3492853 · Cited by 125 · see citing works
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation
Huang, P.-S., Zhang, H., Jiang, R., Stanforth, R., Welbl, J., Rae, J., Maini, V., Yogatama, D., & Kohli, P. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.7 · Cited by 124 · see citing works
A taxonomy and review of generalization research in NLP
Hupkes, D., Giulianelli, M., Dankers, V., Artetxe, M., Elazar, Y., Pimentel, T., Christodoulopoulos, C., Lasri, K., Saphra, N., Sinclair, A., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00729-y · Cited by 121 · see citing works
Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts
Guo, Y., Yang, Y., & Abbasi, A. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.72 · Cited by 120 · see citing works
Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective
Kiritchenko, S., Nejadgholi, I., & Fraser, K. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12590 · Cited by 118 · see citing works
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Zhou, X., Sap, M., Swayamdipta, S., Choi, Y., & Smith, N. A. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.274 · Cited by 116 · see citing works
Li, L., Fan, L., Atreja, S., & Hemphill, L. (2024) · ACM Transactions on the Web · doi:10.1145/3643829 · Cited by 116 · see citing works
Perturbation Sensitivity Analysis to Detect Unintended Model Biases
Prabhakaran, V., Hutchinson, B., & Mitchell, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1578 · Cited by 115 · see citing works
Biswas, S., & Rajan, H. (2021) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3468264.3468536 · Cited by 114 · see citing works
Challenges in Detoxifying Language Models
Welbl, J., Glaese, A., Uesato, J., Dathathri, S., Mellor, J., Hendricks, L. A., Anderson, K., Kohli, P., Coppin, B., & Huang, P.-S. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.210 · Cited by 112 · see citing works
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Beutel, A., Chen, J., Doshi, T., Qian, H., Woodruff, A., Luu, C., Kreitmann, P., Bischof, J., & Chi, E. H. (2019) · Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3306618.3314234 · Cited by 111 · 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
Krešňáková, V., Sarnovský, M., Butka, P., & Machová, K. (2020) · Applied Sciences · doi:10.3390/app10238631 · Cited by 108 · see citing works
Using attribution to decode binding mechanism in neural network models for chemistry
McCloskey, K., Taly, A., Monti, F., Brenner, M. P., & Colwell, L. J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1820657116 · Cited by 105 · see citing works
Incorporating Priors with Feature Attribution on Text Classification
Liu, F., & Avci, B. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1631 · Cited by 104 · see citing works
Demoting Racial Bias in Hate Speech Detection
Xia, M., Field, A., & Tsvetkov, Y. (2020) · International Workshop on Natural Language Processing for Social Media (SocialNLP) · doi:10.18653/v1/2020.socialnlp-1.2 · Cited by 102 · see citing works
Biswas, S., & Rajan, H. (2020) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3368089.3409704 · Cited by 99 · see citing works
Zhu, Y., Sheng, Q., Cao, J., Li, S., Wang, D., & Zhuang, F. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531816 · Cited by 99 · see citing works
Stereotypical Bias Removal for Hate Speech Detection Task using Knowledge-based Generalizations
Badjatiya, P., Gupta, M., & Varma, V. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313504 · Cited by 97 · see citing works
HONEST: Measuring Hurtful Sentence Completion in Language Models
Nozza, D., Bianchi, F., & Hovy, D. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.191 · Cited by 95 · see citing works
A Survey of Race, Racism, and Anti-Racism in NLP
Field, A., Blodgett, S. L., Talat, Z., & Tsvetkov, Y. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.149 · Cited by 94 · see citing works
Pairwise Fairness for Ranking and Regression
Narasimhan, H., Cotter, A., Gupta, M. R., & Wang, S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5970 · Cited by 91 · see citing works
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Solaiman, I., & Dennison, C. (2021) · arXiv · doi:10.48550/arxiv.2106.10328 · Cited by 90 · see citing works
Kennedy, B., Atari, M., Davani, A. M., Yeh, L., Omrani, A., Kim, Y., Coombs, K., Havaldar, S., Portillo-Wightman, G., Gonzalez, E., et al. (2022) · Language Resources and Evaluation · doi:10.1007/s10579-021-09569-x · Cited by 90 · see citing works
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Kuwatly, H. A., Wich, M., & Groh, G. (2020) · Workshop on Online Abuse and Harms (WOAH) · doi:10.18653/v1/2020.alw-1.21 · Cited by 88 · see citing works
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Kim, S.-H., Razi, A., Stringhini, G., Wiśniewski, P., & Choudhury, M. D. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3476066 · Cited by 86 · see citing works
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Garg, T., Masud, S., Suresh, T., & Chakraborty, T. (2023) · ACM Computing Surveys · doi:10.1145/3580494 · Cited by 86 · see citing works
Markl, N. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533117 · Cited by 86 · see citing works
Kennedy, C. J., Bacon, G., Sahn, A., & von Vacano, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.10277 · Cited by 84 · see citing works
Empirical Analysis of Multi-Task Learning for Reducing Identity Bias in Toxic Comment Detection
Vaidya, A., Mai, F., & Ning, Y. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7334 · Cited by 83 · see citing works
Evaluating Models' Local Decision Boundaries via Contrast SetsPreprint
Gardner, M., Artzi, Y., Basmova, V., Berant, J., Bogin, B., Chen, S., Dasigi, P., Dua, D., Elazar, Y., Gottumukkala, A., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.02709 · Cited by 83 · see citing works
Identifying and Improving Disability Bias in GPT-Based Resume Screening
Glazko, K., Mohammed, Y., Kosa, B., Potluri, V., & Mankoff, J. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3630106.3658933 · Cited by 85 · see citing works
Detoxifying Language Models Risks Marginalizing Minority Voices
Xu, A., Pathak, E., Wallace, E., Gururangan, S., Sap, M., & Klein, 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.190 · Cited by 81 · see citing works
Rieder, B., & Skop, Y. (2021) · Big Data & Society · doi:10.1177/20539517211046181 · Cited by 81 · see citing works
Metamorphic Testing and Certified Mitigation of Fairness Violations in NLP Models
Ma, P., Wang, S., & Liu, J. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/64 · Cited by 80 · see citing works
Unintended Bias in Misogyny Detection
Nozza, D., Volpetti, C., & Fersini, E. (2019) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3350546.3352512 · Cited by 79 · see citing works
Survey on Machine Learning Biases and Mitigation Techniques
Siddique, S., Haque, M. A., George, R., Gupta, K. D., Gupta, D., & Faruk, M. J. H. (2023) · Digital · doi:10.3390/digital4010001 · Cited by 78 · see citing works
Counterfactual Inference for Text Classification Debiasing
Chen, Q., Feng, F., Wen, L., Ma, C., & Xie, P. (2021) · doi:10.18653/v1/2021.acl-long.422 · Cited by 78 · see citing works
Lam, M. S., Gordon, M., Metaxa, D., Hancock, J. T., Landay, J. A., & Bernstein, M. S. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555625 · Cited by 78 · see citing works
Artifical Intelligence and Bias: Challenges, Implications, and Remedies
Min, A. (2023) · Journal Of Social Research · doi:10.55324/josr.v2i11.1477 · Cited by 78 · see citing works
- Authors
- Dixon, L., Li, J., Sorensen, J., Thain, N., & Vasserman, L.
- Affiliations
- Google (United States) (US)
- Venue
- Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society
- Publisher
- ACM
- Published
- 2018-12-27
- Pages
- 67-73
- ISSN
- 3065-8365 (ISSN-L)
- OpenAlex
- W2791170418
- Cited by
- 674
- References
- 11
- Citations by year
- 2026: 43 · 2025: 77 · 2024: 70 · 2023: 103 · 2022: 120 · 2021: 132 · 2020: 76 · 2019: 50 · 2018: 3
- FWCI
- 115.48
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the AAAI/ACM Conference on AI Ethics and Society (publishedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Keywords
- text classification, bias mitigation, demographic bias, toxicity detection, Wikipedia talk pages, imbalanced training data, algorithmic fairness
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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