Measuring and Mitigating Unintended Bias in Text Classification

An introduction to ROC analysis

Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879

Equality of Opportunity in Supervised Learning

Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536

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

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

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

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

Inherent Tradeoffs in the Fair Determination of Risk Scores

Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603197 · Cited by 350

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

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

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

Catalyst of hate? Ethnic insulting on YouTube in the aftermath of terror attacks in France, Germany and the United Kingdom 2014–2017

Czymara, C. S., Dochow-Sondershaus, S., Drouhot, L. G., Şimşek, M., & Spörlein, C. (2022) · Journal of Ethnic and Migration Studies · doi:10.1080/1369183x.2022.2100552 · Cited by 28

Recognizing misogynous memes: Biased models and tricky archetypes

Rizzi, G., Gasparini, F., Saibene, A., Rosso, P., & Fersini, E. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103474 · Cited by 30

Towards Multimodal Sentiment Analysis Debiasing via Bias Purification

Yang, D., Li, M., Xiao, D., Liu, Y., Yang, K., Chen, Z., Wang, Y., Zhai, P., Li, K. J., & Zhang, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73636-0_27 · Cited by 30

The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification

Liu, H., Jin, W., Karimi, H., Liu, Z., & Tang, J. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.7 · Cited by 33

Latent Hatred: A Benchmark for Understanding Implicit Hate SpeechPreprint

ElSherief, M., Ziems, C., Muchlinski, D., Anupindi, V., Seybolt, J., Choudhury, M. D., & Yang, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.05322 · Cited by 28

Exploring content moderation in the decentralised web

Anaobi, I. H., Raman, A., Castro, I., Zia, H. B., Cristofaro, E. D., Sastry, N., & Gareth Tyson, A. (2021) · ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT) · doi:10.1145/3485983.3494838 · Cited by 27

SenSeI: Sensitive Set Invariance for Enforcing Individual FairnessPreprint

Yurochkin, M., & Sun, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.14168 · Cited by 40

Measuring and Improving Model-Moderator Collaboration using Uncertainty Estimation

Kivlichan, I., Lin, Z. J., Liu, J. Z., & Vasserman, L. (2021) · Workshop on Online Abuse and Harms (WOAH) · doi:10.18653/v1/2021.woah-1.5 · Cited by 26

Domain adaptation for bias mitigation in affective computing: use cases for facial emotion recognition and sentiment analysis systems

Singhal, P., Gokhale, S., Shah, A., Jain, D. K., Walambe, R. A., Ekárt, A., & Kotecha, K. V. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-06659-1 · Cited by 26

Towards Understanding Gender Bias in Relation Extraction

Gaut, A., Sun, T., Tang, S., Huang, Y., Qian, J., ElSherief, M., Zhao, J., Mirza, D., Belding, E., Chang, K.-W., et al. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.265 · Cited by 26

Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection

Balkır, E., Nejadgholi, I., Fraser, K., & Kiritchenko, S. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.192 · Cited by 30

Detoxifying Text with MaRCo: Controllable Revision with Experts and Anti-Experts

Hallinan, S., Liu, A., Choi, Y., & Sap, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.21 · Cited by 36

Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets

Ousidhoum, N. D., Song, Y., & Yeung, D. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.199 · Cited by 26

Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production SystemsPreprint

Bakalar, C., Barreto, R. D. C., Bergman, S., Bogen, M., Chern, B., Corbett‐Davies, S., Hall, M. A., Kloumann, I. M., Lam, M. S., Candela, J. Q., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.06172 · Cited by 40

Utilizing subjectivity level to mitigate identity term bias in toxic comments classification

Zhao, Z., Zhang, Z., & Hopfgartner, F. (2022) · Online Social Networks and Media · doi:10.1016/j.osnem.2022.100205 · Cited by 26

Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors

Nejadgholi, I., Fraser, K., & Kiritchenko, S. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.378 · Cited by 24

Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets

Zhang, G., Bai, B., Liang, J., Bai, K., Chang, S., Yu, M., Zhu, C., & Zhao, T. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1435 · Cited by 25

FuzzE: Fuzzy Fairness Evaluation of Offensive Language Classifiers on African-American English

Rios, A. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5434 · Cited by 23

Characterizing Variation in Toxic Language by Social Context

Radfar, B., Shivaram, K., & Culotta, A. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7366 · Cited by 25

Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms

Neumann, T., De‐Arteaga, M., & Fazelpour, S. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533205 · Cited by 29

Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue modelsPreprint

Smith, E. M., & Williams, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03300 · Cited by 33

Tackling racial bias in automated online hate detection: Towards fair and accurate detection of hateful users with geometric deep learning

Ahmed, Z., Vidgen, B., & Hale, S. A. (2022) · EPJ Data Science · doi:10.1140/epjds/s13688-022-00319-9 · Cited by 22

Features or Spurious Artifacts? Data-centric Baselines for Fair and Robust Hate Speech Detection

Ramponi, A., & Tonelli, S. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.221 · Cited by 22

A machine learning model for predicting, diagnosing, and mitigating health disparities in hospital readmission

Raza, S. (2022) · Healthcare Analytics · doi:10.1016/j.health.2022.100100 · Cited by 24

Fairness-Aware Structured Pruning in Transformers

Zayed, A., Mordido, G., Shabanian, S., Baldini, I., & Chandar, S. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i20.30256 · Cited by 25

Selective Classification Can Magnify Disparities Across GroupsPreprint

Jones, E., Sagawa, S., Koh, P. W., Kumar, A., & Liang, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.14134 · Cited by 34

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop

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Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variations

Hartmann, D., Oueslati, A., Staufer, D., Pohlmann, L., Munzert, S., & Heuer, H. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713998 · Cited by 29

Mechanized Significance and Machine Learning: Why It Became Thinkable and Preferable to Teach Machines to Judge the World

Mendon-Plasek, A. (2020) · Springer eBooks · doi:10.1007/978-3-030-56286-1_2 · Cited by 23

Reducing Unintended Bias of ML Models on Tabular and Textual Data

Alves, G., Amblard, M., Bernier, F., Couceiro, M., & Napoli, A. (2021) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa53316.2021.9564112 · Cited by 25

Examining and mitigating gender bias in text emotion detection task

Odbal, O., Zhang, G., & Ananiadou, S. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.057 · Cited by 21

Benchmarking Post-Hoc Interpretability Approaches for Transformer-based Misogyny Detection

Attanasio, G., Nozza, D., Pastor, E., & Hovy, D. (2022) · doi:10.18653/v1/2022.nlppower-1.11 · Cited by 21

Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection

Sen, I., Samory, M., Wagner, C., & Augenstein, I. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.347 · Cited by 21

Mitigating Biases in Toxic Language Detection through Invariant Rationalization

Chuang, Y.-S., Gao, M., Luo, H., Glass, J. R., Lee, H.-Y., Chen, Y.-N., & Li, S.-W. D. (2021) · Workshop on Online Abuse and Harms (WOAH) · doi:10.18653/v1/2021.woah-1.12 · Cited by 24

Toward a better trade-off between performance and fairness with kernel-based distribution matchingPreprint

Prost, F., Qian, H., Chen, Q., Ed, H., Chen, J., & Beutel, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.11779 · Cited by 21

Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)

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Does algorithmic content moderation promote democratic discourse? Radical democratic critique of toxic language AI

Oh, D., & Downey, J. (2024) · Information Communication & Society · doi:10.1080/1369118x.2024.2346531 · Cited by 20

Learning Disentangled Textual Representations via Statistical Measures of Similarity

Colombo, P., Staerman, G., Noiry, N., & Piantanida, P. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.187 · Cited by 20

Directions for NLP Practices Applied to Online Hate Speech Detection

Fortuna, P., Domínguez, M., Wanner, L., & Talat, Z. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.809 · Cited by 20

Crowdsourcing Subjective Annotations Using Pairwise Comparisons Reduces Bias and Error Compared to the Majority-vote Method

Narimanzadeh, H., Badie-Modiri, A., Smirnova, I., & Chen, T. H. Y. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610183 · Cited by 20

Trapped in the search box: An examination of algorithmic bias in search engine autocomplete predictions

Lin, C., Gao, Y., Ta, N., Li, K., & Fu, H. (2023) · Telematics and Informatics · doi:10.1016/j.tele.2023.102068 · Cited by 20

How Hate Speech Varies by Target Identity: A Computational Analysis

Yoder, M. M., Ng, L. H. X., Brown, D. W., & Carley, K. M. (2022) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2022.conll-1.3 · Cited by 20

Refining Language Models with Compositional ExplanationsPreprint

Yao, H., Chen, Y., Ye, Q., Jin, X., & Ren, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.10415 · Cited by 20

The Effect of Content Moderation on Online and Offline Hate: Evidence from Germany's NetzDGPreprint

Durán, R. J., Müller, K., & Schwarz, C. R. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4230296 · Cited by 20

A Large-Scale English Multi-Label Twitter Dataset for Cyberbullying and Online Abuse Detection

Salawu, S., Lumsden, J., & He, Y. (2021) · Workshop on Online Abuse and Harms (WOAH) · doi:10.18653/v1/2021.woah-1.16 · Cited by 19

Detecting Unintended Social Bias in Toxic Language Datasets

Sahoo, N., Gupta, H., & Bhattacharyya, P. (2022) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2022.conll-1.10 · Cited by 25

Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools

Black, E., Naidu, R. C. A., Ghani, R., Rodolfa, K. T., Ho, D. E., & Heidari, H. (2023) · ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) · doi:10.1145/3617694.3623259 · Cited by 19

Auditing GPT's Content Moderation Guardrails: Can ChatGPT Write Your Favorite TV Show?

Mahomed, Y., Crawford, C. M., Gautam, S., Friedler, S. A., & Metaxa, D. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658932 · Cited by 25

Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics

Chu, Y.-W., Hosseinalipour, S., Tenorio, E., Cruz, L., Douglas, K. A., Lan, A., & Brinton, C. G. (2024) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2024.3407716 · Cited by 19

Limitations of Pinned AUC for Measuring Unintended BiasPreprint

Borkan, D., Dixon, L., Li, J., Sorensen, J. S., Thain, N., & Vasserman, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.02088 · Cited by 19

Potential Pitfalls With Automatic Sentiment Analysis: The Example of Queerphobic Bias

Ungless, E. L., Roß, B., & Belle, V. (2023) · Social Science Computer Review · doi:10.1177/08944393231152946 · Cited by 20

Hate Speech Classifiers are Culturally Insensitive

Lee, N., Jung, C., & Oh, A. H. (2023) · Workshop on Cross-Cultural Considerations in NLP (C3NLP) · doi:10.18653/v1/2023.c3nlp-1.5 · Cited by 18

Generating Contextual Variables From Web-Based Data for Health Research: Tutorial on Web Scraping, Text Mining, and Spatial Overlay Analysis

Gálvez-Hernández, P., Gónzalez-Viana, A., Paz, L. G., Shankardass, K., & Muntaner, C. (2023) · JMIR Public Health and Surveillance · doi:10.2196/50379 · Cited by 18

People who share encounters with racism are silenced online by humans and machines, but a guideline-reframing intervention holds promise

Lee, C., Gligorić, K., Kalluri, P., Harrington, M., Durmus, E., Sanchez, K. L., San, N., Tse, D., Zhao, X., Hamedani, M. G., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2322764121 · Cited by 26

Machine Learning Model Cards Transparency Review : Using model card toolkit

Wadhwani, A., & Jain, P. (2020) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon50868.2020.9362382 · Cited by 19

The untapped potential of mining news media events for understanding environmental change

Buckingham, K. C., Brandt, J., Anderson, W., do Amaral, L. F., & Singh, R. (2020) · Current Opinion in Environmental Sustainability · doi:10.1016/j.cosust.2020.08.015 · Cited by 18

The Economics of Content Moderation: Theory and Experimental Evidence from Hate Speech on TwitterPreprint

Jiménez-Durán, R. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4590147 · Cited by 18

Gender-tuning: Empowering Fine-tuning for Debiasing Pre-trained Language Models

Ghanbarzadeh, S., Huang, Y., Palangi, H., Cruz Moreno, R., & Khanpour, H. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.336 · Cited by 17

Debiasing Vandalism Detection Models at Wikidata

Heindorf, S., Scholten, Y., Engels, G., & Potthast, M. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313507 · Cited by 17

NLPR@SRPOL at SemEval-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier

Seganti, A., Sobol, H., Orlova, I., Kim, H., Staniszewski, J., Krumholc, T., & Koziel, K. (2019) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s19-2126 · Cited by 20

Persian offensive language detection

Kebriaei, E., Homayouni, A., Faraji, R., Razavi, A., Shakery, A., Faili, H., & Yaghoobzadeh, Y. (2023) · Machine Learning · doi:10.1007/s10994-023-06370-5 · Cited by 18

The Role of Protected Class Word Lists in Bias Identification of Contextualized Word Representations

Sedoc, J., & Ungar, L. (2019) · Workshop on Gender Bias in Natural Language Processing (GeBNLP) · doi:10.18653/v1/w19-3808 · Cited by 19

Reducing Unintended Identity Bias in Russian Hate Speech Detection

Zueva, N., Kabirova, M., & Kalaidin, P. (2020) · Workshop on Online Abuse and Harms (WOAH) · doi:10.18653/v1/2020.alw-1.8 · Cited by 21

Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification

Pruksachatkun, Y., Krishna, S., Dhamala, J., Gupta, R., & Chang, K.-W. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.294 · Cited by 21

Gender bias in legal corpora and debiasing it

Sevim, N., Şahi̇nuç, F., & Koç, A. (2022) · Natural Language Engineering · doi:10.1017/s1351324922000122 · Cited by 22

Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future

Yang, L., Song, Y., Ren, X., Lyu, C., Wang, Y., Zhuo, J., Liu, L., Wang, J., Foster, J., & Zhang, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.276 · Cited by 21

Mitigating social biases of pre-trained language models via contrastive self-debiasing with double data augmentation

Li, Y., Du, M., Song, R., Wang, X., Sun, M., & Wang, Y. (2024) · Artificial Intelligence · doi:10.1016/j.artint.2024.104143 · Cited by 16

From Toxicity in Online Comments to Incivility in American News: Proceed with Caution

Hede, A., Agarwal, O., Lu, L., Mutz, D. C., & Nenkova, A. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.225 · Cited by 16

Bias and comparison framework for abusive language datasets

Wich, M., Eder, T., Kuwatly, H. A., & Groh, G. (2021) · AI and Ethics · doi:10.1007/s43681-021-00081-0 · Cited by 16

What Is the Intended Usage Context of This Model? An Exploratory Study of Pre-Trained Models on Various Model Repositories

Gong, L., Zhang, J., Wei, M., Zhang, H., & Huang, Z. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3569934 · Cited by 17

Evaluating Errors and Improving Performance of ChatGPT

Biswas, S. S. (2023) · International Journal of Clinical and Medical Education Research · doi:10.33140/ijcmer.02.06.05 · Cited by 17

Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design

Bergman, A. S., Abercrombie, G., Spruit, S. L., Hovy, D., Dinan, E., Boureau, Y.-L., & Rieser, V. (2022) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2022.sigdial-1.4 · Cited by 16

Six Attributes of Unhealthy ConversationPreprint

Price, I., Gifford-Moore, J., Fleming, J., Musker, S., Roichman, M., Sylvain, G., Thain, N., Dixon, L., & Sorensen, J. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.07410 · Cited by 20

Artificial Financial IntelligencePreprint

Magnuson, W. J. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3403712 · Cited by 16

Innovative Guardrails for Generative AI: Designing an Intelligent Filter for Safe and Responsible LLM Deployment

Shvetsova, O. A., Katalshov, D., & Lee, S.-K. (2025) · Applied Sciences · doi:10.3390/app15137298 · Cited by 18

Fair classification via domain adaptation: A dual adversarial learning approach

Liang, Y., Chen, C., Tian, T., & Shu, K. (2023) · Frontiers in Big Data · doi:10.3389/fdata.2022.1049565 · Cited by 15

Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairness

Ashktorab, Z., Hoover, B., Agarwal, M., Dugan, C., Geyer, W., Yang, H., & Yurochkin, M. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581227 · Cited by 21

Using hierarchical text classification to investigate the utility of machine learning in automating online analyses of wildlife exploitation

Hunter, S. B., Mathews, F., & Weeds, J. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102076 · Cited by 15

LOGAN: Local Group Bias Detection by Clustering

Zhao, J., & Chang, K.-W. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.155 · Cited by 15

Towards an Enhanced Understanding of Bias in Pre-trained Neural Language Models: A Survey with Special Emphasis on Affective Bias

Anoop, K. R., Gangan, M. P., Deepak, P., & Lajish, V. L. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-4453-6_2 · Cited by 17

A Near-Optimal Algorithm for Debiasing Trained Machine Learning ModelsPreprint

Alabdulmohsin, I., & Lučić, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.12887 · Cited by 17

Combating Online Hate Speech: The Impact of Legislation on TwitterPreprint

Andres, R., & Slivko, O. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.4013662 · Cited by 18

Synthetic data distillation enables the extraction of clinical information at scale

Woo, E. G., Burkhart, M. C., Alsentzer, E., & Beaulieu‐Jones, B. K. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01681-4 · Cited by 18

Identifying Implicitly Abusive Remarks about Identity Groups using a Linguistically Informed Approach

Wiegand, M., Eder, E., & Ruppenhofer, J. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.410 · Cited by 14

A Human-centered Evaluation of a Toxicity Detection API: Testing Transferability and Unpacking Latent Attributes

Muralikumar, M. D., Yang, Y. S., & McDonald, D. W. (2023) · ACM Transactions on Social Computing · doi:10.1145/3582568 · Cited by 14

ChatGPT impacts in programming education: A recent literature overview that debates ChatGPT responses

Anagnostopoulos, C. (2023) · F1000Research · doi:10.12688/f1000research.141958.1 · Cited by 17

Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you?

Choenni, R., Shutova, E., & van Rooij, R. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.111 · Cited by 20

Debiasing pretrained text encoders by paying attention to paying attention

Gaci, Y., Benatallah, B., Casati, F., & Benabdeslem, K. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.651 · Cited by 14

Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness

Zayed, A., Parthasarathi, P., Mordido, G., Palangi, H., Shabanian, S., & Chandar, S. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26706 · Cited by 19

Beyond plain toxic: building datasets for detection of flammable topics and inappropriate statements

Babakov, N., Logacheva, V., & Panchenko, A. (2023) · Language Resources and Evaluation · doi:10.1007/s10579-023-09682-z · Cited by 16

AAA: Fair Evaluation for Abuse Detection Systems Wanted

Calabrese, A., Bevilacqua, M., Roß, B., Tripodi, R., & Roberto Navigli, A. (2021) · ACM Web Science Conference (WebSci) · doi:10.1145/3447535.3462484 · Cited by 13

COLD: Annotation scheme and evaluation data set for complex offensive language in English

Palmer, A., Carr, C., Robinson, M., & Sanders, J. (2020) · LDV-Forum/Journal for language technology and computational linguistics · doi:10.21248/jlcl.34.2020.222 · Cited by 13

MUSENET: Multi-Scenario Learning for Repeat-Aware Personalized Recommendation

Xu, S., Li, L., Yao, Y., Chen, Z., Wu, H., Lu, Q., & Tong, H. (2023) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3539597.3570414 · Cited by 13

Quantifying 60 Years of Gender Bias in Biomedical Research with Word Embeddings

Rios, A., Joshi, R., & Shin, H. (2020) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2020.bionlp-1.1 · Cited by 15

Rissanen Data Analysis: Examining Dataset Characteristics via Description LengthPreprint

Perez, E., Kiela, D., & Cho, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.03872 · Cited by 13

Dbias: Detecting biases and ensuring Fairness in news articlesPreprint

Raza, S., Reji, D. J., & Ding, C. (2022) · Research Square · doi:10.21203/rs.3.rs-1356281/v1 · Cited by 15

Social Media vs. Democracy: Evidence from the January 6th Insurrection

Müller, K., Schwarz, C. R., & Shen, Z. (2026) · CESifo · doi:10.65864/qlfi14z2yh · Cited by 14

Which Stereotypes Are Moderated and Under-Moderated in Search Engine Autocompletion?

Leidinger, A., & Rogers, R. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594062 · Cited by 12

Dual-Teacher De-Biasing Distillation Framework for Multi-Domain Fake News Detection

Li, J., Feng, X., Gu, T., & Chang, L. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00279 · Cited by 12

Gender bias detection on hate speech classification: an analysis at feature-level

Nascimento, F. R. S., Cavalcanti, G. D. C., & Costa‐Abreu, M. D. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10841-8 · Cited by 13

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 419

Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting

Zhang, G., Bai, B., Zhang, J., Bai, K., Zhu, C., & Zhao, T. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.380 · Cited by 65

Debiasing Personal Identities in Toxicity ClassificationPreprint

Zorian, A. A., & Bikkanur, C. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.05757 · Cited by 3

Mitigating Unwanted Biases with Adversarial Learning

Zhang, B. H., Lemoine, B., & Mitchell, M. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3278721.3278779 · Cited by 1,387

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