Eddie Ungless

Eddie Ungless

Looking for post-docs/ research scientist positions. A collection of my thoughts on the topics of ethics, fairness and social bias in NLP and beyond.

Reading Lists

Reading List for Teaching Presentation, University of Aberdeen
  • Jaimeen Ahn and Alice Oh. 2021. Mitigating Language-Dependent Ethnic Bias in BERT. arXiv:2109.05704 [cs]. arXiv: 2109.05704.
  • Federico Bianchi, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza, Tatsunori Hashimoto, Dan Jurafsky, James Zou, and Aylin Caliskan. 2023. Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pages 1493–1504, New York, NY, USA. Association for Computing Machinery.
  • Su Lin Blodgett and Brendan O’Connor. 2017. Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English. arXiv.
  • Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. In Advances in Neural Information Processing Systems, volume 29. Curran Associates, Inc.
  • Joy Buolamwini and Timnit Gebru. 2018. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Conference on Fairness, Accountability and Transparency, pages 77–91. PMLR.
  • Sara Hooker. 2021. Moving beyond “algorithmic bias is a data problem.” Patterns, 2(4):100241.
  • Andrew D. Selbst, danah boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi. 2019. Fairness and Abstraction in Sociotechnical Systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency, pages 59–68, New York, NY, USA. Association for Computing Machinery.
  • Preethi Seshadri, Sameer Singh, and Yanai Elazar. 2024. The Bias Amplification Paradox in Text-to-Image Generation. In Kevin Duh, Helena Gomez, and Steven Bethard, editors, Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 6367–6384, Mexico City, Mexico. Association for Computational Linguistics.
  • Deven Santosh Shah, H. Andrew Schwartz, and Dirk Hovy. 2020. Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5248–5264, Online. Association for Computational Linguistics.
  • Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. 2019. The Woman Worked as a Babysitter: On Biases in Language Generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3405–3410, Hong Kong, China. Association for Computational Linguistics.
  • Andrew Silva, Pradyumna Tambwekar, and Matthew Gombolay. 2021. Towards a Comprehensive Understanding and Accurate Evaluation of Societal Biases in Pre-Trained Transformers. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2383–2389, Online. Association for Computational Linguistics.
  • Harini Suresh and John Guttag. 2021. A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle. In Equity and Access in Algorithms, Mechanisms, and Optimization, pages 1–9, New York, NY, USA. Association for Computing Machinery.
  • Yarden Tal, Inbal Magar, and Roy Schwartz. 2022. Fewer Errors, but More Stereotypes? The Effect of Model Size on Gender Bias. In Christian Hardmeier, Christine Basta, Marta R. Costa-jussà, Gabriel Stanovsky, and Hila Gonen, editors, Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP), pages 112–120, Seattle, Washington. Association for Computational Linguistics.
  • Prasetya Ajie Utama, Nafise Sadat Moosavi, and Iryna Gurevych. 2020. Towards Debiasing NLU Models from Unknown Biases. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7597–7610, Online. Association for Computational Linguistics.
  • Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2017. Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints. In Martha Palmer, Rebecca Hwa, and Sebastian Riedel, editors, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2979–2989, Copenhagen, Denmark. Association for Computational Linguistics.
  • Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018. Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods. In Marilyn Walker, Heng Ji, and Amanda Stent, editors, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 15–20, New Orleans, Louisiana. Association for Computational Linguistics.

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