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* 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](https://aclanthology.org/2022.naacl-main.97/)][](t-biases.md)[](t-evaluation.md)[](type-published.md)
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* 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](https://aclanthology.org/2023.woah-1.14/)][](t-biases.md)[](t-model-issues.md)[](type-published.md)
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* 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](https://aclanthology.org/2022.trustnlp-1.8/)][](t-biases.md)[](type-published.md)
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* 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](https://aclanthology.org/2022.naacl-main.192/)][](t-biases.md)[](type-published.md)
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* 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](https://aclanthology.org/2022.acl-long.301.pdf)]
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* 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](https://www.frontiersin.org/articles/10.3389/frai.2022.826207)][](t-biases.md)[](type-published.md)
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* 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](https://aclanthology.org/2022.acl-long.132.pdf)]
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* 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](https://aclanthology.org/2022.blackboxnlp-1.18/)][](t-biases.md)[](t-model-issues.md)[](type-published.md)
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* 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](https://aclanthology.org/2022.acl-long.583.pdf)]
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* 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](https://arxiv.org/abs/2103.03417)]
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* 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](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)]
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* 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](https://aclanthology.org/2021.acl-long.149/)]
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* 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](https://aclanthology.org/2021.acl-long.50/)][](t-biases.md)[](type-published.md)
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* 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](https://www.aclweb.org/anthology/2020.acl-main.485)]
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* 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](https://www.aclweb.org/anthology/2020.acl-main.702)]
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* 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](https://www.aclweb.org/anthology/2020.cl-2.7)]
* Fraser, K.C., Kiritchenko, S., Balkir, E. (2022) Does Moral Code Have a Moral Code? Probing Delphi's Moral Philosophy. In Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022), pages 26–42, Seattle, U.S.A. Association for Computational Linguistics. [[paper](https://aclanthology.org/2022.trustnlp-1.3/)][](t-general-resources.md)[](type-published.md)
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* Mohammad S. (2022). Ethics Sheets for AI Tasks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8368–8379, Dublin, Ireland. Association for Computational Linguistics. [[paper](https://aclanthology.org/2022.acl-long.573.pdf)]
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AAAI/ACM Conference on AI, Ethics, and Society, Association for Computing Machinery, 2021, 287-297. [[paper](https://dl.acm.org/doi/pdf/10.1145/3461702.3462563)]
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* Kiritchenko, S., Nejadgholi, I., and Fraser, K. C. (2021). Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective. Journal of Artificial Intelligence Research, 71: 431-478, July 2021. doi:10.1613/jair.1.12590. [[paper](https://www.jair.org/index.php/jair/article/view/12590/26695)][](t-general-resources.md)[](type-published.md)
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* Hagendorff, T. The Ethics of AI Ethics: An Evaluation of Guidelines Minds & Machines, 2020, 30, 99-120. [[paper](https://link.springer.com/content/pdf/10.1007/s11023-020-09517-8.pdf)]
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* Bogdan Kulynych, Rebekah Overdorf, Carmela Troncoso, and Seda Gürses. 2020. POTs: protective optimization technologies. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). Association for Computing Machinery, New York, NY, USA, 177–188. DOI:https://doi.org/10.1145/3351095.3372853. [[paper](https://arxiv.org/pdf/1806.02711.pdf)]
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* Monteiro, M. (2019). Ruined by design: How designers destroyed the world, and what we can do to fix it. Mule Design. 
* 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](https://aclanthology.org/2023.woah-1.14/)][](t-biases.md)[](t-model-issues.md)[](type-published.md)
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* 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](https://aclanthology.org/2022.blackboxnlp-1.18/)][](t-biases.md)[](t-model-issues.md)[](type-published.md)
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* 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](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)]
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* 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](https://aclanthology.org/2021.acl-long.149/)]
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* Floridi, L., Chiriatti, M. GPT-3: Its Nature, Scope, Limits, and Consequences. Minds & Machines 30, 681–694 (2020). https://doi.org/10.1007/s11023-020-09548-1[[paper](https://link.springer.com/content/pdf/10.1007/s11023-020-09548-1.pdf)]
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