Computer Science > Computation and Language
arXiv:2204.06827 (cs)
[Submitted on 14 Apr 2022 (v1), last revised 16 May 2022 (this version, v2)]
Title:How Gender Debiasing Affects Internal Model Representations, and Why It Matters
View a PDF of the paper titled How Gender Debiasing Affects Internal Model Representations, and Why It Matters, by Hadas Orgad and 2 other authors
View PDFAbstract:Common studies of gender bias in NLP focus either on extrinsic bias measured by model performance on a downstream task or on intrinsic bias found in models' internal representations. However, the relationship between extrinsic and intrinsic bias is relatively unknown. In this work, we illuminate this relationship by measuring both quantities together: we debias a model during downstream fine-tuning, which reduces extrinsic bias, and measure the effect on intrinsic bias, which is operationalized as bias extractability with information-theoretic probing. Through experiments on two tasks and multiple bias metrics, we show that our intrinsic bias metric is a better indicator of debiasing than (a contextual adaptation of) the standard WEAT metric, and can also expose cases of superficial debiasing. Our framework provides a comprehensive perspective on bias in NLP models, which can be applied to deploy NLP systems in a more informed manner. Our code and model checkpoints are publicly available.
| Comments: | Accepted to NAACL 2022 |
| Subjects: | Computation and Language (cs.CL) |
| MSC classes: | 68T50 |
| ACM classes: | I.2.7 |
| Cite as: | arXiv:2204.06827 [cs.CL] |
| (orarXiv:2204.06827v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2204.06827 arXiv-issued DOI via DataCite |
Submission history
From: Hadas Orgad [view email][v1] Thu, 14 Apr 2022 08:54:15 UTC (7,608 KB)
[v2] Mon, 16 May 2022 21:27:21 UTC (7,617 KB)
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View a PDF of the paper titled How Gender Debiasing Affects Internal Model Representations, and Why It Matters, by Hadas Orgad and 2 other authors
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