[26] Yan Liu, Yan Gao, Zhe Su, Xiaokang Chen, Elliott Ash, and Jian-Guang Lou. Uncovering and categorizing social biases in text-to-SQL. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13573-13584, Toronto, Canada, July 2023. Association for Computational Linguistics.
[27] Li Lucy and David Bamman. Gender and representation bias in GPT-3 generated stories. In Proceedings of the Third Workshop on Narrative Understanding, pages 48–55, Virtual, June 2021. Association for Computational Linguistics.
[28] Lingjuan Lyu, Han Yu, Xingjun Ma, Chen Chen, Lichao Sun, Jun Zhao, Qiang Yang, and S Yu Philip. Privacy and robustness in federated learning: Attacks and defenses. IEEE transactions on neural networks and learning systems, 2022.
[29] Nicholas Meade, Elinor Poole-Dayan, and Siva Reddy. An empirical survey of the effectiveness of debiasing techniques for pre-trained language models, 2021.
[30] Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. A survey on bias and fairness in machine learning. arXiv: Learning, 2019.
[31] Moin Nadeem, Anna Bethke, and Siva Reddy. StereoSet: Measuring stereotypical bias in pretrained language models. 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), Online, August 2021. Association for Computational Linguistics.
[32] Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R. Bowman. CrowS-pairs: A challenge dataset for measuring social biases in masked language models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, November 2020. Association for Computational Linguistics.
[33] Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. Codegen: An open large language model for code with multi-turn program synthesis. 2022.
[34] Nedjma Ousidhoum, Xinran Zhao, Tianqing Fang, Yangqiu Song, and Dit-Yan Yeung. Probing toxic content in large pre-trained language models. 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), Online, August 2021. Association for Computational Linguistics.
[35] Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, and Samuel Bowman. BBQ: A hand-built bias benchmark for question answering. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2086-2105, Dublin, Ireland, May 2022. Association for Computational Linguistics.
[36] Junaid Qadir, Mohammad Qamar Islam, and Ala Al-Fuqaha. Toward accountable human-centered ai: rationale and promising directions. Journal of Information, Communication and Ethics in Society, 2022.
[37] Inioluwa Deborah Raji, Andrew Smart, Rebecca N White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes. Closing the ai accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 conference on fairness, accountability, and transparency, pages 33-44, 2020.
[38] Sebastian Scher and Andreas Trügler. Robustness of machine learning models beyond adversarial attacks. arXiv preprint arXiv:2204.10046, 2022.
[39] Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. The woman worked as a babysitter: On biases in language generation. empirical methods in natural language processing, 2019.
[40] Emily Sheng, Kai-Wei Chang, Prem Natarajan, and Nanyun Peng. Towards Controllable Biases in Language Generation. In Findings of the Association for Computational Linguistics: EMNLP 2020, Online, November 2020. Association for Computational Linguistics.