Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models

Daniel de Vassimon Manela · David Errington · Thomas Fisher · Boris van Breugel · Pasquale Minervini

59 citations0/0 files storedConference of the European Chapter of the Association for Computational Linguistics

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Ethics and Social Impacts of AI Topic Modeling Gender Studies in Language

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Daniel de Vassimon Manela, David Errington, Thomas Fisher, Boris van Breugel, Pasquale Minervini. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.

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2019HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case 2017Semantics derived automatically from language corpora contain human-like biases 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings 2022Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women *2021StereoSet: Measuring stereotypical bias in pretrained language models 2019Mitigating Gender Bias in Natural Language Processing: Literature Review 2019On Measuring Social Biases in Sentence Encoders 2020CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models

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2023Probing Pre-Trained Language Models for Cross-Cultural Differences in Values 2022Measuring Fairness with Biased Rulers: A Comparative Study on Bias Metrics for Pre-trained Language Models 2023Gender bias in transformers: A comprehensive review of detection and mitigation strategies 2023Stereotypes in ChatGPT: an empirical study 2023Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgetting

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