[18] Matan Halevy, Camille Harris, Amy Bruckman, Diyi Yang, and Ayanna Howard. Mitigating racial biases in toxic language detection with an equity-based ensemble framework. In Equity and Access in Algorithms, Mechanisms, and Optimization, EAAMO '21, New York, NY, USA, 2021. Association for Computing Machinery.
[19] Dan Hendrycks, Nicholas Carlini, John Schulman, and Jacob Steinhardt. Unsolved problems in ml safety, 2022.
[20] Fan Huang, Haewoon Kwak, and Jisun An. Is chatgpt better than human annotators? potential and limitations of chatgpt in explaining implicit hate speech. In Companion Proceedings of the ACM Web Conference 2023, WWW '23 Companion, page 294-297, New York, NY, USA, 2023. Association for Computing Machinery.
[21] Florian Jaton. Assessing biases, relaxing moralism: On ground-truthing practices in machine learning design and application. _Big Data and Society_, 8(1), 2021.
[22] Jan Kocón, Alicja Figas, Marcin Gruza, Daria Puchalska, Tomasz Kajdanowicz, and Przemysław Kazienko. Offensive, aggressive, and hate speech analysis: From data-centric to human-centered approach. Information Processing Management, 58(5):102643, 2021.
[23] Jan Kocón, Marcin Gruza, Julita Bielaniewicz, Damian Grimling, Kamil Kanclerz, Piotr Miłkowski, and Przemysław Kazienko. Learning personal human biases and representations for subjective tasks in natural language processing. In 2021 IEEE International Conference on Data Mining (ICDM), pages 1168-1173, 2021.
[24] Allison Lahnala, Charles Welch, Béla Neuendorf, and Lucie Flek. Mitigating toxic degeneration with empathetic data: Exploring the relationship between toxicity and empathy. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4926-4938, Seattle, United States, July 2022. Association for Computational Linguistics.
[25] Elisa Leonardelli, Stefano Menini, Alessio Palmero Aprosio, Marco Guerini, and Sara Tonelli. Agreeing to disagree: Annotating offensive language datasets with annotators' disagreement. arXiv preprint arXiv:2109.13563, 2021.
[26] Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee. Hatexplain: A benchmark dataset for explainable hate speech detection. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 14867-14875, 2021.
[27] John Pavlopoulos, Jeffrey Sorensen, Lucas Dixon, Nithum Thain, and Ion Androutsopoulos. Toxicity detection: Does context really matter? arXiv preprint arXiv:2006.00998, 2020.
[28] Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving. Red teaming language models with language models, 2022.
[29] Stephen Roller, Y-Lan Boureau, Jason Weston, Antoine Bordes, Emily Dinan, Angela Fan, David Gunning, Da Ju, Margaret Li, Spencer Poff, Pratik Ringshia, Kurt Shuster, Eric Michael Smith, Arthur Szlam, Jack Urbanek, and Mary Williamson. Open-domain conversational agents: Current progress, open problems, and future directions, 2020.
[30] Paul Röttger, Bertie Vidgen, Dirk Hovy, and Janet B Pierrehumbert. Two contrasting data annotation paradigms for subjective nlp tasks. arXiv preprint arXiv:2112.07475, 2021.
[31] Elayne Ruane, Abeba Birhane, and Anthony Ventresque. Conversational ai: Social and ethical considerations. In AICS, pages 104-115, 2019.
[32] Nihar Sahoo, Himanshu Gupta, and Pushpak Bhattacharyya. Detecting unintended social bias in toxic language datasets, 2022.
[33] Marta Sandri, Elisa Leonardelli, Sara Tonelli, and Elisabetta Ježek. Why don't you do it right? analysing annotators' disagreement in subjective tasks. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 2420-2433, 2023.