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There is a growing body of work that proposes methods for mitigating bias in machine learning systems.
Logistic regression in rare events data
Gary King and Langche Zeng. 2001 · 2001
Earlier work this paper cites.
k-means++: The advantages of careful seeding
David Arthur and Sergei Vassilvitskii. 2007 · 2007
Earlier work this paper cites.
Frequently occurring surnames in the 2010 census
Joshua Comenetz. 2016 · 2010
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012 · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Big data’s disparate impact
Solon Barocas and Andrew D Selbst. 2016 · 2016
Earlier work this paper cites.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016 · 2016
Earlier work this paper cites.
Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016 · 2016
Earlier work this paper cites.
Productivity and selection of human capital with machine learning
Aaron Chalfin, Oren Danieli, Andrew Hillis, Zubin Jelveh, Michael Luca, Jens Ludwig, and Sendhil Mullainathan. 2016 · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Nati Srebro, et al. 2016 · 2016
Cited alongside, same era.
Data-driven discrimination at work
Pauline T Kim. 2016 · 2016
Cited alongside, same era.
Gender shades: intersectional phenotypic and demographic evaluation of face datasets and gender classifiers
Joy Adowaa Buolamwini. 2017 · 2017
Cited alongside, same era.
Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan. 2017 · 2017
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova. 2017 · 2017
Cited alongside, same era.
Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P Gummadi. 2017 · 2017
Later among the works it cites.
Baby names from social security card applications - national level data
Social Security Administration. 2018 · 2018
Later among the works it cites.
Maya Gupta, Andrew Cotter, Mahdi Milani Fard, and Serena Wang. 2018 · 2018
Later among the works it cites.
Demographic aspects of first names
Konstantinos Tzioumis. 2018 · 2018
Later among the works it cites.
Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. 2018 · 2018
Later among the works it cites.
Bias in bios: A case study of semantic representation bias in a high-stakes setting
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Alexandra Chouldechova and Max G’Sell. 2017 · 2017
Cited alongside, same era.
UCI machine learning repository
Dua Dheeru and Efi Karra Taniskidou. 2017 · 2017
Cited alongside, same era.
Deep learning for healthcare: review, opportunities and challenges
Riccardo Miotto, Fei Wang, Shuang Wang, Xiaoqian Jiang, and Joel T Dudley. 2017 · 2017
Cited alongside, same era.
Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai. 2019 · 2019
Closest in time.
Improving fairness in machine learning systems: What do industry practitioners need?
K. Holstein, J. Wortman Vaughan, H. Daumé III, M. Dudík, and H. Wallach. 2019 · 2019
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What are the biases in my word embedding?
Nathaniel Swinger, Maria De-Arteaga, IV Heffernan, Neil Thomas, Mark DM Leiserson, and Adam Tauman Kalai. 2019 · 2019
Closest in time.