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Filtered Union Bibliography

This automatically-generated file contains references from the main union bibliography that have been filtered for a single tag. Do not edit this file; instead, please update the main bibliography and tag references appropriately to have them show up here. Thank you!

The papers are listed in the same order as the main bibliography; e.g., by year of publication / release; then by surname / name of the first author.

Biases

  • Chen, Y., Raghuram, V. C., Mattern, J., Mihalcea, R., & Jin, Z. (2025). Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 4984–5004, Albuquerque, New Mexico. Association for Computational Linguistics. [paper] Biases published

  • Ducel, F., Hiebel, N., Ferret, O., Fort, K., & Névéol, A. (2025). “Women do not have heart attacks!" Gender Biases in Automatically Generated Clinical Cases in French. Findings of the Association for Computational Linguistics: NAACL 2025:7145–7159. [paper] Biases published

  • Jin, Z., Levine, S., Kleiman-Weiner, M., Piatti, G., Liu, J., Adauto, F.G., Ortu, F., Strausz, A., Sachan, M., Mihalcea, R., Choi, Y., & Scholkopf, B. (2024). Language Model Alignment in Multilingual Trolley Problems. International Conference on Learning Representations. [paper] Biases Language Diversity published

  • Mitchell, M., Attanasio, G., Baldini, I., Clinciu, M., Clive, J., Delobelle, P., Dey, M., Hamilton, S., Dill, T., Doughman, J., Dutt, R., Ghosh, A., Zosa Forde, J., Holtermann, C., Kaffee, L. A., Laud, T., Lauscher, A., Lopez-Davila, R. L., Masoud, M., Nangia, N., Ovalle, A., Pistilli, G., Radev, D., Savoldi, B., Raheja, V., Qin, J., Ploeger, E., Subramonian, A., Dhole, K., Sun, K., Djanibekov, A., Mansurov, J., Yin, K., Villa Cueva, E., Mukherjee, S., Huang, J., Shen, X., Gala, J., Al-Ali, H., Djanibekov, T., Mukhituly, N., Nie, S., Sharma, S., Stanczak, K., Szczechla, E., Timponi Torrent, T., Tunuguntla, D., Viridiano, M., Van Der Wal, O., Yakefu, A., Névéol, A., Zhang, M., Zink, S., & Talat, Z. (2025). SHADES: Towards a Multilingual Assessment of Stereotypes in Large Language Models. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2025:11995–12041. [paper] Biases published

  • Curry, A. C., Attanasio, G., Talat, Z. & Hovy, D. (2024, August). Classist Tools: Social Class Correlates with Performance in NLP. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12643–12655, Bangkok, Thailand. Association for Computational Linguistics. [paper] Biases published

  • Ducel F, Névéol A, Fort K. (2024). “You’ll be a nurse, my son!” Automatically assessing gender biases in autoregressive language models in French and Italian. Language Resources and Evaluation. Springer, Berlin Heidelberg, Germany. 2024:1-29 [paper] Biases Evaluation published

  • Helm, P., Bella, G., Koch, G. et al. (2024). Diversity and language technology: how language modeling bias causes epistemic injustice. Ethics and Information Technology. [paper] Biases published

  • Hofmann, V., Kalluri, P.R., Jurafsky, D. et al. (2024). AI generates covertly racist decisions about people based on their dialect. Nature 633, 147–154. https://doi.org/10.1038/s41586-024-07856-5 Biases published

  • Jin, Z., Heil, N., Liu, J., Dhuliawala, S., Qi, Y., Schölkopf, B., Mihalcea, R., & Sachan, M. (2024). Implicit Personalization in Language Models: A Systematic Study. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 12309–12325, Miami, Florida, USA. Association for Computational Linguistics. [paper] Biases published

  • Jin, Z., Levine, S., Kleiman-Weiner, M., Piatti, G., Liu, J., Adauto, F.G., Ortu, F., Strausz, A., Sachan, M., Mihalcea, R., Choi, Y., & Scholkopf, B. (2024). Language Model Alignment in Multilingual Trolley Problems. International Conference on Learning Representations. [paper] Biases Language Diversity published

  • Kantharuban, A., Milbauer, J., Strubell, E., & Neubig, G. (2024). Stereotype or personalization? user identity biases chatbot recommendations [paper] Biases preprint

  • Gonçalves, G. & Strubell, E. (2023). Understanding the Effect of Model Compression on Social Bias in Large Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2663–2675, Singapore. Association for Computational Linguistics. [paper] Biases Model Issues published

  • Kirk, H. R., Vidgen, B., Röttger, P., Thrush, T., & Hale, S. A. (2023). Hatemoji: A test suite and adversarially-generated dataset for benchmarking and detecting emoji-based hate. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. (NAACL '23') 10.18653/v1/2022.naacl-main.97 [paper] Biases Evaluation published

  • Nejadgholi, I., Kiritchenko, S., Fraser, K. C., & Balkir, E. (2023) Concept-Based Explanations to Test for False Causal Relationships Learned by Abusive Language Classifiers. In Proceedings of the 7th Workshop on Online Abuse and Harms (WOAH), pages 138–149, Toronto, Canada. Association for Computational Linguistics. [paper] Biases Model Issues published

  • Parmar, M., Mishra, S., Geva, M., & Baral, C. (2023). Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1779–1789. [paper] Biases published

  • Vicente, L., & Matute, H. (2023). Humans inherit artificial intelligence biases. Scientific Reports, 13(1), 15737. [paper] Biases published

  • Balkir, E., Kiritchenko, S., Nejadgholi, I., Fraser, K.C. (2022) Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models. In Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022), pages 80–92, Seattle, U.S.A. Association for Computational Linguistics. [paper] Biases published

  • Balkir, E., Nejadgholi, I., Fraser, K.C., Kiritchenko, S. (2022). Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2672–2686, Seattle, United States. Association for Computational Linguistics. [paper] Biases published

  • Chalkidis I., Pasini T., Zhang S., Tomada L., Schwemer S., & Søgaard A. (2022). FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4389–4406, Dublin, Ireland. Association for Computational Linguistics. [paper] Biases published

  • D'Ignazio, C. (2022). The Urgency of Moving From Bias to Power. European Data Protection Law Review. Volume 8, Issue 4 (pp. 451 - 454). [paper] Biases published

  • Fraser, K.C., Kiritchenko, S., Nejadgholi, I. (2022). Computational Modelling of Stereotype Content in Text. Frontiers in Artificial Intelligence, 5, 2022. doi:10.3389/frai.2022.826207. [paper] Biases published

  • Meade N., Poole-Dayan E., & Reddy S. (2022). An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1878–1898, Dublin, Ireland. Association for Computational Linguistics. [paper] Biases published

  • Miceli, M., Posada, J., & Yang, T. (2022). Studying up machine learning data: Why talk about bias when we mean power?. Proceedings of the ACM on Human-Computer Interaction, 6(GROUP), 1-14. [paper] Biases published

  • Nejadgholi, I., Balkir, E., Fraser, K.C., & Kiritchenko, S. (2022). Towards Procedural Fairness: Uncovering Biases in How a Toxic Language Classifier Uses Sentiment Information.In Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pages 225–237, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics. [paper] Biases Model Issues published

  • Névéol, A., Dupont, Y., Bezançon, J., & Fort, K. (2022). French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8521–8531, Dublin, Ireland. Association for Computational Linguistics. [paper] Biases published

  • Talat, Z., Névéol, A., Biderman, S., Clinciu, M., Dey, M., Longpre, S., Luccioni, S., Masoud, M., Mitchell, M., Radev, D., Sharma, S., Subramonian, A., Tae, J., Tan, S., Tunuguntla, D. & Van Der Wal, O. (2022). You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings. In Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models, pages 26–41, virtual+Dublin. Association for Computational Linguistics. [paper] Biases published

  • Aka, O., Burke, K., Bäuerle, A., Greer, C., & Mitchell, M. (2021). Measuring Model Biases in the Absence of Ground Truth. DOI:10.1145/3461702.3462557. AIES '21: AAAI/ACM Conference on AI, Ethics, and Society. [paper] Biases published

  • Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?🦜. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610-623). doi:10.1145/3442188.3445922 [paper] Model Issues Biases published

  • Dev, S., Monajatipoor, M., Ovalle, A., Subramonian, A., Phillips, J., and Chang, K. (2021). Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1968–1994. [paper] Biases published

  • Field, A., Blodgett, S. L., Talat, Z., & Tsvetkov, Y. (2021, August). A Survey of Race, Racism, and Anti-Racism in NLP. In 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), pages 1905–1925, Online. Association for Computational Linguistics. doi:10.18653/v1/2021.acl-long.149 [paper] Model Issues Biases published

  • Fraser K. C., Nejadgholi, I. and Kiritchenko, S. (2021). Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model. In 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), pages 600–616, Online. Association for Computational Linguistics. [paper] Biases published

  • Hooker, S. (2021). Moving beyond “algorithmic bias is a data problem”. Patterns, 2(4).[paper] Biases published

  • Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020). Language (technology) is power: A critical survey of "bias" in NLP. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5454–5476, Online. Association for Computational Linguistics. doi:10.18653/v1/2020.acl-main.485. [paper] Biases published

  • Mohammad, S. M. (2020, July). Gender gap in natural language processing research: Disparities in authorship and citations. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. doi:10.18653/v1/2020.acl-main.702 [paper] Biases published

  • Nissim, M., van Noord, R., & van der Goot, R. (2020). Fair is better than sensational: Man is to doctor as woman is to doctor. Computational Linguistics, 46(2), 487-497. doi:10.1162/coli_a_00379 [paper] Biases published

  • Garimella, A., Banea, C., Hovy, D., & Mihalcea, R. (2019, July). Women's syntactic resilience and men's grammatical luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3493-3498). [paper] Biases published

  • Sap, M., Gabriel, S., Qin, L., Jurafsky, D., Smith, N. A., & Choi, Y. (2019). Social bias frames: Reasoning about social and power implications of language. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5477–5490, Online. Association for Computational Linguistics. [paper] Biases published

  • Curry, A. C., & Rieser, V. (2018, June). # MeToo Alexa: How conversational systems respond to sexual harassment. In Proceedings of the second ACL workshop on ethics in natural language processing (pp. 7-14). [paper] Biases published

  • Fort, K., & Névéol, A. (2018, January). Présence et représentation des femmes dans le traitement automatique des langues en France. In Penser la Recherche en Informatique comme pouvant être Située, Multidisciplinaire Et Genrée (PRISME-G). [paper] Biases published

  • Kiritchenko S. and Mohammad S. (2018). Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 43–53, New Orleans, Louisiana. Association for Computational Linguistics. [paper] Biases published

  • Schluter, N. (2018). The glass ceiling in NLP. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 2793-2798). doi:10.18653/v1/D18-1301 [paper] Biases published

  • Koolen, C. & van Cranenburgh, A. These are not the Stereotypes You are Looking For: Bias and Fairness in Authorial Gender Attribution. In Proceedings of the first ACL workshop on ethics in natural language processing (pp. 12-22). [paper] Biases published

  • Rudinger, R., May, C., & Van Durme, B. (2017, April). Social bias in elicited natural language inferences. In Proceedings of the First ACL Workshop on Ethics in Natural Language Processing (pp. 74-79). [paper] Biases published

  • Larson, J., Angwin, J., & Parris, T. (2016). Breaking the black box: How machines learn to be racist. ProPublica. [paper] Biases post