It’s All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution

Rowan Hall Maudslay · Hila Gonen · Ryan Cotterell · Simone Teufel

107 citations0/0 files storedConference on Empirical Methods in Natural Language Processing (EMNLP)

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Ethics and Social Impacts of AI Natural Language Processing Techniques Topic Modeling

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Rowan Hall Maudslay, Hila Gonen, Ryan Cotterell, Simone Teufel. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

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References

1998Measuring individual differences in implicit cognition: The implicit association test.1998Measuring individual differences in implicit cognition: The implicit association test.2017Semantics derived automatically from language corpora contain human-like biases 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings 2002Harvesting implicit group attitudes and beliefs from a demonstration web site.2020Gender Bias in Neural Natural Language Processing 2019Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them 2019Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them

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2020Fair Is Better than Sensational: Man Is to Doctor as Woman Is to Doctor 2021Sustainable Modular Debiasing of Language Models 2020Does Gender Matter? Towards Fairness in Dialogue Systems 2021Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics 2022Quantifying and alleviating political bias in language models

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