The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Adversarial Removal of Demographic Attributes from Text Data
Open accessAbstract
Recent advances in Representation Learning and Adversarial Training seem to succeed in removing unwanted features from the learned representation.We show that demographic information of authors is encoded in-and can be recovered from-the intermediate representations learned by text-based neural classifiers.The implication is that decisions of classifiers trained on textual data are not agnostic to-and likely condition on-demographic attributes.When attempting to remove such demographic information using adversarial training, we find that while the adversarial component achieves chance-level development-set accuracy during training, a post-hoc classifier, trained on the encoded sentences from the first part, still manages to reach substantially higher classification accuracies on the same data.This behavior is consistent across several tasks, demographic properties and datasets.We explore several techniques to improve the effectiveness of the adversarial component.Our main conclusion is a cautionary one: do not rely on the adversarial training to achieve invariant representation to sensitive features.
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Cites
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Equality of Opportunity in Supervised Learning
Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016) · arXiv · doi:10.48550/arxiv.1607.06520 · Cited by 1,615
Mitigating Unwanted Biases with Adversarial Learning
Zhang, B. H., Lemoine, B., & Mitchell, M. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3278721.3278779 · Cited by 1,387
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Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1323 · Cited by 829 · cited 1×
“By using the raw text as is, a discrimination issue might arise, as textual information can be predictive of some demographic factors (Hovy et al., 2015) and author’s attributes might correlate with target variables (Zhao et al., 2017).”
BackgroundDemographic dialectal variation in social media: a case study of African-American English
Blodgett, S. L., Green, L., & O'Connor, B. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1120 · Cited by 291 · cited 1×
“…race, gender, and many other social factors of the author (Kop-pel et al., 2002; Burger et al., 2011; Nguyen et al., 2013; Weren et al., 2014; Verhoeven and Daele-mans, 2014; Rangel et al., 2016; Verhoeven et al., 2016; Blodgett et al., 2016), or even the audience of the text (Voigt et al., 2018).”
Background” How old do you think I am?” A study of language and age in Twitter
Nguyen, D., Gravel, R., Trieschnigg, D., & Meder, T. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14381 · Cited by 287
Cited by
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
Sakaguchi, K., Le Bras, R., Bhagavatula, C., & Choi, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6399 · Cited by 579
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Dathathri, S., Madotto, A., Lan, J., Hung, J., Frank, E., Molino, P., Yosinski, J., & Liu, R. (2019) · arXiv · doi:10.48550/arxiv.1912.02164 · Cited by 508
Mitigating Gender Bias in Natural Language Processing: Literature Review
Sun, T., Gaut, A., Tang, S., Huang, Y., ElSherief, M., Zhao, J., Mirza, D., Belding, E., Chang, K.-W., & Wang, W. Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1159 · Cited by 492
WinoGrande: an adversarial winograd schema challenge at scale
Sakaguchi, K., Bras, R. L., Bhagavatula, C., & Choi, Y. (2021) · Communications of the ACM · doi:10.1145/3474381 · Cited by 356
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
Borkan, D., Dixon, L., Sorensen, J., Thain, N., & Vasserman, L. (2019) · Companion Proceedings of The 2019 World Wide Web Conference · doi:10.1145/3308560.3317593 · Cited by 309
Metadata
- Authors
- Elazar, Y., & Goldberg, Y.
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Published
- 2018
- Pages
- 11-21
- OpenAlex
- W2888161220
- Cited by
- 237
- References
- 11
- FWCI
- 57.03
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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