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The goal of this work is to help mitigate the already existing gender wage gap by supplying unbiased job recommendations based on resumes from job seekers.
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Efficient estimation of word representations in vector space,
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Generative adversarial networks,
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Non-binary or genderqueer genders,
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Man is to computer programmer as woman is to homemaker? Debiasing word embeddings,
T. Bolukbasi, K.-W. Chang, J. Y. Zou, V. Saligrama, A. T. Kalai, · 2016
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Censoring representations with an adversary,
H. Edwards, A. J. Storkey, · 2016
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Gender inequality: Nonbinary transgender people in the workplace,
S. Davidson, · 2016
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Data decisions and theoretical implications when adversarially learning fair representations,
A. Beutel, J. Chen, Z. Zhao, E. H. Chi, · 2017
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Semantics derived automatically from language corpora contain human-like biases,
A. Caliskan, J. Bryson, A. Narayanan, · 2017
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Part-time employment, the gender wage gap and the role of wage-setting institutions: Evidence from 11 European countries,
E. Matteazzi, A. Pailhé, A. Solaz, · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding,
J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, · 2018
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Learning adversarially fair and transferable representations,
D. Madras, E. Creager, T. Pitassi, R. Zemel, · 2018
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A. A. Fogarty, L. Zheng, Gender ambiguity in the workplace: Transgender and gender-diverse discrimination, ABC-CLIO, 2018. URL: https://publisher.abc-clio.com/9781440863233/
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Cited alongside, same era.
Mitigating unwanted biases with adversarial learning,
B. H. Zhang, B. Lemoine, M. Mitchell, · 2018
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Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them,
H. Gonen, Y. Goldberg, · 2019
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Fairness GAN: Generating datasets with fairness properties using a generative adversarial network,
P. Sattigeri, S. C. Hoffman, V. Chenthamarakshan, K. R. Varshney, · 2019
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Discriminated by an algorithm: A systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development,
A. Köchling, M. C. Wehner, · 2020
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Ethnic employment gaps of graduates in the Netherlands,
P. Bisschop, B. ter Weel, J. Zwetsloot, · 2020
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A snapshot of the frontiers of fairness in machine learning,
A. Chouldechova, A. Roth, · 2020
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Discrimination against Turkish minorities in Germany and the Netherlands: field experimental evidence on the effect of diagnostic information on labour market outcomes,
L. Thijssen, B. Lancee, S. Veit, R. Yemane, · 2019
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Labour market discrimination against Moroccan minorities in the Netherlands and Spain: A cross-national and cross-regional comparison,
M. Ramos, L. Thijssen, M. Coenders, · 2019
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Discrimination through optimization: How Facebook’s ad delivery can lead to biased outcomes,
M. Ali, P. Sapiezynski, M. Bogen, A. Korolova, A. Mislove, A. Rieke, · 2019
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FairGAN+: Achieving fair data generation and classification through generative adversarial nets,
D. Xu, S. Yuan, L. Zhang, X. Wu, · 2019
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Responsible team players wanted: an analysis of soft skill requirements in job advertisements,
F. Calanca, L. Sayfullina, L. Minkus, C. Wagner, E. Malmi, · 2019
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It’s all in the name: Mitigating gender bias with name–based counterfactual data substitution,
R. Hall Maudslay, H. Gonen, R. Cotterell, S. Teufel, · 2019
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Learning invariant representations from EEG via adversarial inference,
O. Özdenizci, Y. Wang, T. Koike-Akino, D. Erdoğmuş, · 2020
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Sticky floors or glass ceilings? The role of human capital, working time flexibility and discrimination in the gender wage gap,
G. Ciminelli, C. Schwellnus, B. Stadler, · 2021
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Dual constraints and adversarial learning for fair recommenders,
H. Liu, N. Zhao, X. Zhang, H. Lin, L. Yang, B. Xu, Y. Lin, W. Fan, · 2021
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A survey on bias and fairness in machine learning,
N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, A. Galstyan, · 2021
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Does robustness improve fairness? Approaching fairness with word substitution robustness methods for text classification,
Y. Pruksachatkun, S. Krishna, J. Dhamala, R. Gupta, K.-W. Chang, · 2021
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Gendered information in resumes and its role in algorithmic and human hiring bias,
P. Parasurama, J. Sedoc, · 2022
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