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.
- Kirk, H. R., Vidgen, B., Röttger, P., Thrush, T., and 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]
- 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]
- 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]
- 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]
- Chalkidis I., Pasini T., Zhang S., Tomada L., Schwemer S., and 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]
- 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]
- Meade N., Poole-Dayan E., and 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]
- 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]
- Névéol A., Dupont Y., Bezançon J., and 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- Larson, J., Angwin, J., & Parris, T. (2016). Breaking the black box: How machines learn to be racist. ProPublica. [paper]
