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Title
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How far can it go? On intrinsic gender bias mitigation for text classification
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Author
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Abstract
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| To mitigate gender bias in contextualized language models, different intrinsic mitigation strategies have been proposed, alongside many bias metrics. Considering that the end use of these language models is for downstream tasks like text classification, it is important to understand how these intrinsic bias mitigation strategies actually translate to fairness in downstream tasks and the extent of this. In this work, we design a probe to investigate the effects that some of the major intrinsic gender bias mitigation strategies have on downstream text classification tasks. We discover that instead of resolving gender bias, intrinsic mitigation techniques and metrics are able to hide it in such a way that significant gender information is retained in the embeddings. Furthermore, we show that each mitigation technique is able to hide the bias from some of the intrinsic bias measures but not all, and each intrinsic bias measure can be fooled by some mitigation technique, but not all. We confirm experimentally, that none of the intrinsic mitigation techniques used without any other fairness intervention is able to consistently impact extrinsic bias. We recommend that intrinsic bias mitigation techniques should be combined with other fairness interventions for downstream tasks. |
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Language
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English
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Source (book)
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Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2023), May 2-6, Dubrovnik, Croatia
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Publication
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Association for Computational Linguistics
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2023
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ISBN
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978-1-959429-44-9
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DOI
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10.18653/V1/2023.EACL-MAIN.248
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Volume/pages
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p. 3418-3433
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ISI
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001181056902026
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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