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Liberalism-oriented political philosophy reasons that all individuals should be treated equally independently of their protected characteristics.
Justice as fairness
John Rawls · 1958
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Arthur S Miller and Ronald F Howell · 1959
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Robert J Aumann and Jacques H Dreze · 1974
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Michael A Fligner and George E Policello · 1981
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Elizabeth R DeLong, David M DeLong, and Daniel L Clarke-Pearson · 1988
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Richard J Arneson · 1989
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Gerald A Cohen · 1989
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Free to choose
Milton Friedman, Rose D Friedman, and Rose D Friedman · 1990
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Justice as fairness: Political not metaphysical
John Rawls · 1991
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Neutrality in constitutional law (with special reference to pornography, abortion, and surrogacy)
Cass R Sunstein · 1992
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A value for n-person games
Lloyd S Shapley · 1997
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Will Kymlicka · 2002
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The Shapley value
Eyal Winter · 2002
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Confidence intervals for the area under the ROC curve
Corinna Cortes and Mehryar Mohri · 2004
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A studentized permutation test for the non-parametric behrens-fisher problem
Karin Neubert and Edgar Brunner · 2007
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Discrimination-aware data mining
Dino Pedreschi, Salvatore Ruggieri, and Franco Turini · 2008
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Fairness-aware learning through regularization approach
Toshihiro Kamishima, Shotaro Akaho, and Jun Sakuma · 2011
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake VanderPlas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Edouard Duchesnay · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard S. Zemel · 2012
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ROC curve estimation: An overview
Luzia Gonçalves, Ana Subtil, M Rosário Oliveira, and Patricia de Zea Bermudez · 2014
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Explaining prediction models and individual predictions with feature contributions
Erik Strumbelj and Igor Kononenko · 2014
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On the consistency of AUC pairwise optimization
Wei Gao and Zhi-Hua Zhou · 2015
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Anupam Datta, Shayak Sen, and Yair Zick · 2016
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Censoring representations with an adversary
Harrison Edwards and Amos J. Storkey · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
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Algorithmic decision making and the cost of fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq · 2017
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Inherent trade-offs in the fair determination of risk scores
Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
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Counterfactual fairness
Matt J. Kusner, Joshua R. Loftus, Chris Russell, and Ricardo Silva · 2017
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Distributive justice
Julian Lamont and Christi Favor · 2017
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Revisiting classifier two-sample tests
David Lopez-Paz and Maxime Oquab · 2017
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A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee · 2017
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 2017
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Explainability and fairness in machine learning: Improve fair end-to-end lending for kiva
Alexander Stevens, Peter Deruyck, Ziboud Van Veldhoven, and Jan Vanthienen · 2020
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The many shapley values for model explanation
Mukund Sundararajan and Amir Najmi · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors · 2020
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Bias preservation in machine learning: the legality of fairness metrics under european union non-discrimination law
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2020
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, and Been Kim · 2018
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Towards robust interpretability with self-explaining neural networks
David Alvarez-Melis and Tommi S. Jaakkola · 2018
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How to win a data science competition: Learn from top kagglers - national research university higher school of economics
Alexander Guschin, Dmitry Ulyanov, Mikhail Trofimov, Dmitry Altukhov, and Mario Michaidilis · 2018
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael J. Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
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Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
Scott M Lundberg, Bala Nair, Monica S Vavilala, Mayumi Horibe, Michael J Eisses, Trevor Adams, David E Liston, Daniel King-Wai Low, Shu-Fang Newman, Jerry Kim, et al · 2018
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The intuitive appeal of explainable machines
Andrew D Selbst and Solon Barocas · 2018
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Kaiyu Yang, Klint Qinami, Li Fei-Fei, Jia Deng, and Olga Russakovsky · 2020
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Explaining individual predictions when features are dependent: More accurate approximations to shapley values
Kjersti Aas, Martin Jullum, and Anders Løland · 2021
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Measuring model biases in the absence of ground truth
Osman Aka, Ken Burke, Alex Bäuerle, Christina Greer, and Margaret Mitchell · 2021
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Deep neural networks and tabular data: A survey, 2021
Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, and Gjergji Kasneci · 2021
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Retiring adult: New datasets for fair machine learning
Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt · 2021
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Do we really need deep learning models for time series forecasting?
Shereen Elsayed, Daniela Thyssens, Ahmed Rashed, Lars Schmidt-Thieme, and Hadi Samer Jomaa · 2021
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RATT: leveraging unlabeled data to guarantee generalization
Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter, and Zachary C. Lipton · 2021
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Efficient computation and analysis of distributional shapley values
Yongchan Kwon, Manuel A. Rivas, and James Zou · 2021
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Fairshades: Fairness auditing via explainability in abusive language detection systems
Marta Marchiori Manerba and Riccardo Guidotti · 2021
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Towards the right kind of fairness in AI
Boris Ruf and Marcin Detyniecki · 2021
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Machine learning and the meaning of equal treatment
Joshua Simons, Sophia Adams Bhatti, and Adrian Weller · 2021
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A survey on bias in visual datasets
Simone Fabbrizzi, Symeon Papadopoulos, Eirini Ntoutsi, and Ioannis Kompatsiaris · 2022
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Leveraging unlabeled data to predict out-of-distribution performance
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Marrying fairness and explainability in supervised learning
Przemyslaw A. Grabowicz, Nicholas Perello, and Aarshee Mishra · 2022
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Why do tree-based models still outperform deep learning on typical tabular data?
Léo Grinsztajn, Edouard Oyallon, and Gaël Varoquaux · 2022
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Generalized demographic parity for group fairness
Zhimeng Jiang, Xiaotian Han, Chao Fan, Fan Yang, Ali Mostafavi, and Xia Hu · 2022
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2022
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Explanation shift: Detecting distribution shifts on tabular data via the explanation space
Carlos Mougan, Klaus Broelemann, Gjergji Kasneci, Thanassis Tiropanis, and Steffen Staab · 2022
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Contrastive counterfactual fairness in algorithmic decision-making
Ece Çigdem Mutlu, Niloofar Yousefi, and Özlem Özmen Garibay · 2022
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The shapley value in machine learning
Benedek Rozemberczki, Lauren Watson, Péter Bayer, Hao-Tsung Yang, Oliver Kiss, Sebastian Nilsson, and Rik Sarkar · 2022
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Kaggle: What is adversarial validation?
Carl Mcbride Ellis · 2023
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Interventional SHAP values and interaction values for piecewise linear regression trees
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