Computer Science > Computation and Language
arXiv:2205.06135 (cs)
[Submitted on 12 May 2022]
Title:Fair NLP Models with Differentially Private Text Encoders
View a PDF of the paper titled Fair NLP Models with Differentially Private Text Encoders, by Gaurav Maheshwari and 3 other authors
View PDFAbstract:Encoded text representations often capture sensitive attributes about individuals (e.g., race or gender), which raise privacy concerns and can make downstream models unfair to certain groups. In this work, we propose FEDERATE, an approach that combines ideas from differential privacy and adversarial training to learn private text representations which also induces fairer models. We empirically evaluate the trade-off between the privacy of the representations and the fairness and accuracy of the downstream model on four NLP datasets. Our results show that FEDERATE consistently improves upon previous methods, and thus suggest that privacy and fairness can positively reinforce each other.
| Comments: | submitted to: ACL-ARR 2022 (February) -this https URL |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2205.06135 [cs.CL] |
| (orarXiv:2205.06135v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2205.06135 arXiv-issued DOI via DataCite |
Submission history
From: Gaurav Maheshwari [view email][v1] Thu, 12 May 2022 14:58:38 UTC (8,392 KB)
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View a PDF of the paper titled Fair NLP Models with Differentially Private Text Encoders, by Gaurav Maheshwari and 3 other authors
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