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Recently, much attention has been paid to the societal impact of AI, especially concerns regarding its fairness.
Co-ranking Authors and Documents in a Heterogeneous Network
Zhou, D.; Orshanskiy, S. A.; Zha, H.; and Giles, C. L. 2007 · 2007
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Three naive Bayes approaches for discrimination-free classification
Calders, T.; and Verwer, S. 2010 · 2010
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Fairness through awareness
Dwork, C.; Hardt, M.; Pitassi, T.; Reingold, O.; and Zemel, R. 2012 · 2012
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Learning fair representations
Zemel, R.; Wu, Y.; Swersky, K.; Pitassi, T.; and Dwork, C. 2013 · 2013
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Deepwalk: Online learning of social representations
Perozzi, B.; Al-Rfou, R.; and Skiena, S. 2014 · 2014
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Certifying and removing disparate impact
Feldman, M.; Friedler, S. A.; Moeller, J.; Scheidegger, C.; and Venkatasubramanian, S. 2015 · 2015
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Facebook as a research tool for the social sciences: Opportunities, challenges, ethical considerations, and practical guidelines
Kosinski, M.; Matz, S. C.; Gosling, S. D.; Popov, V.; and Stillwell, D. 2015 · 2015
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Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks
Angwin, J.; Larson, J.; Mattu, S.; and Kirchner, L. 2016 · 2016
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Bolukbasi, T.; Chang, K.-W.; Zou, J. Y.; Saligrama, V.; and Kalai, A. T. 2016 · 2016
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Equality of opportunity in supervised learning
Hardt, M.; Price, E.; and Srebro, N. 2016 · 2016
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An Uncertainty-Aware Approach for Exploratory Microblog Retrieval
Liu, M.; Liu, S.; Zhu, X.; Liao, Q.; Wei, F.; and Pan, S. 2016 · 2016
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Semantics derived automatically from language corpora contain human-like biases
Caliskan, A.; Bryson, J. J.; and Narayanan, A. 2017 · 2017
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Learning community embedding with community detection and node embedding on graphs
Cavallari, S.; Zheng, V. W.; Cai, H.; Chang, K. C.-C.; and Cambria, E. 2017 · 2017
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Conscientious classification: A data scientist’s guide to discrimination-aware classification
d’Alessandro, B.; O’Neil, C.; and LaGatta, T. 2017 · 2017
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metapath2vec: Scalable representation learning for heterogeneous networks
Dong, Y.; Chawla, N. V.; and Swami, A. 2017 · 2017
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Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning
Fu, T.-y.; Lee, W.-C.; and Lei, Z. 2017 · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N.; and Welling, M. 2017 · 2017
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Controllable invariance through adversarial feature learning
Xie, Q.; Dai, Z.; Du, Y.; Hovy, E.; and Neubig, G. 2017 · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Attenuating bias in word vectors
Dev, S.; and Phillips, J. 2019 · 2019
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Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them
Gonen, H.; and Goldberg, Y. 2019 · 2019
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Mitigating Demographic Biases in Social Media-Based Recommender Systems
Islam, R.; Keya, K. N.; Pan, S.; and Foulds, J. 2019 · 2019
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Fairwalk: towards fair graph embedding
Rahman, T.; Surma, B.; Backes, M.; and Zhang, Y. 2019 · 2019
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Heterogeneous Information Network Embedding for Recommendation
Shi, C.; Hu, B.; Zhao, W. X.; and Yu, P. S. 2019 · 2019
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Heterogeneous network embedding enabling accurate disease association predictions
Xiong, Y.; Guo, M.; Ruan, L.; Kong, X.; Tang, C.; Zhu, Y.; and Wang, W. 2019 · 2019
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Buolamwini, J.; and Gebru, T. 2018 · 2018
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Amazon scraps secret AI recruiting tool that showed bias against women
Dastin, J. 2018 · 2018
Cited alongside, same era.
Aspem: Embedding learning by aspects in heterogeneous information networks
Shi, Y.; Gui, H.; Zhu, Q.; Kaplan, L.; and Han, J. 2018 · 2018
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Graph Attention Networks
Velickovic, P.; Cucurull, G.; Casanova, A.; Romero, A.; Liò, P.; and Bengio, Y. 2018 · 2018
Cited alongside, same era.
Shine: Signed heterogeneous information network embedding for sentiment link prediction
Wang, H.; Zhang, F.; Hou, M.; Xie, X.; Guo, M.; and Liu, Q. 2018 · 2018
Cited alongside, same era.
Deep collective classification in heterogeneous information networks
Zhang, Y.; Xiong, Y.; Kong, X.; Li, S.; Mi, J.; and Zhu, Y. 2018 · 2018
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Compositional fairness constraints for graph embeddings
Bose, A.; and Hamilton, W. 2019 · 2019
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Graphsaint: Graph sampling based inductive learning method
Zeng, H.; Zhou, H.; Srivastava, A.; Kannan, R.; and Prasanna, V. 2019 · 2019
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Early 20th‐Century Career Counseling for Women: Contemporary Practice and Research Implications
Alshabani, N.; and Soto, S. 2020 · 2020
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Algorithms of Oppression: How Search Engines Reinforce Racism , volume 22
Noble, S. 2020 · 2020
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Bias in word embeddings
Papakyriakopoulos, O.; Hegelich, S.; Serrano, J. C. M.; and Marco, F. 2020 · 2020
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Debiasing Career Recommendations with Neural Fair Collaborative Filtering
Islam, R.; Keya, K.; Zeng, Z.; Pan, S.; and Foulds, J. 2021 · 2021
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A comprehensive survey on graph neural networks
Wu, Z.; Pan, S.; Chen, F.; Long, G.; Zhang, C.; and Philip, S. Y. 2021 · 2021
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