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Algorithmic fairness has attracted increasing attention in the machine learning community.
An intersectional definition of fairness. In
James R Foulds, Rashidul Islam, Kamrun Naher Keya, and Shimei Pan. 2020 · 1921
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Fairness without demographics in repeated loss minimization. In
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. 2018 · 1938
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Fair inference on outcomes. In
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A Theory of Justice
John Rawls. 1971 · 1971
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Complete identification methods for the causal hierarchy
Ilya Shpitser and Judea Pearl. 2008 · 1979
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Generalized Gini inequality indices
John A Weymark. 1981 · 1981
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Morals by Agreement
David Gauthier. 1987 · 1987
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Equality and equal opportunity for welfare
Richard J Arneson. 1989 · 1989
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Mapping the margins: Intersectionality, identity politics, and violence against women of color
Kimberle Crenshaw. 1990 · 1990
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Independence properties of directed Markov fields
Steffen L Lauritzen, A Philip Dawid, Birgitte N Larsen, and H-G Leimer. 1990 · 1990
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Local Justice: How Institutions Allocate Scarce Goods and Necessary Burdens
Jon Elster. 1992 · 1992
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Will affirmative-action policies eliminate negative stereotypes?
Stephen Coate and Glenn C Loury. 1993 · 1993
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Causation, Prediction, and Search
Peter Spirtes, Clark Glymour, and Richard Scheines. 1993 · 1993
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Directed cyclic graphical representations of feedback models. In
Peter Spirtes. 1995 · 1995
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Causal inference in the presence of latent variables and selection bias. In
Peter Spirtes, Christopher Meek, and Thomas Richardson. 1995 · 1995
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Graphical Models . Vol. 17
Steffen L Lauritzen. 1996 · 1996
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Durable Inequality
Charles Tilly. 1998 · 1998
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What is the point of equality?
Elizabeth Anderson. 1999 · 1999
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What We Owe to Each Other
Thomas Scanlon. 2000 · 2000
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The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek. 2000 · 2000
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Principles of Social Justice
David Miller. 2001 · 2001
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Justice as Fairness: A Restatement
John Rawls. 2001 · 2001
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Optimal structure identification with greedy search
David Maxwell Chickering. 2002 · 2002
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Sovereign Virtue: The Theory and Practice of Equality
Ronald Dworkin. 2002 · 2002
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A general identification condition for causal effects. In
Jin Tian and Judea Pearl. 2002 · 2002
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Markov properties for acyclic directed mixed graphs
Thomas Richardson. 2003 · 2003
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Identifiability of path-specific effects. In
Chen Avin, Ilya Shpitser, and Judea Pearl. 2005 · 2005
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The problem of global justice
Thomas Nagel. 2005 · 2005
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Identifiability in causal bayesian networks: A sound and complete algorithm. In
Yimin Huang and Marco Valtorta. 2006 · 2006
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Levelling the playing field: The idea of equal opportunity and its place in egalitarian thought
Andrew Mason. 2006 · 2006
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A linear non-Gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, Antti Kerminen, and Michael Jordan. 2006 · 2006
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Identification of conditional interventional distributions. In
Ilya Shpitser and Judea Pearl. 2006 · 2006
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What counterfactuals can be tested. In
Ilya Shpitser and Judea Pearl. 2007 · 2007
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Building classifiers with independency constraints. In
Toon Calders, Faisal Kamiran, and Mykola Pechenizkiy. 2009 · 2009
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Fairness and legitimacy in justice, and: Does option luck ever preserve justice
GA Cohen, S de Wijze, MH Kramer, and I Carter. 2009 · 2009
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Causality
Judea Pearl. 2009 · 2009
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On the identifiability of the post-nonlinear causal model. In
Kun Zhang and Aapo Hyvärinen. 2009 · 2009
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The Imperative of Integration
Elizabeth Anderson. 2010 · 2010
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Fairness-aware learning through regularization approach. In
Toshihiro Kamishima, Shotaro Akaho, and Jun Sakuma. 2011 · 2011
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Responsibility and Distributive Justice
Carl Knight and Zofia Stemplowska. 2011 · 2011
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Kernel-based conditional independence test and application in causal discovery. In
Kun Zhang, Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. 2011 · 2011
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Fairness through awareness. In
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012 · 2012
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Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders. 2012 · 2012
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Quantifying explainable discrimination and removing illegal discrimination in automated decision making
Faisal Kamiran, Indrė Žliobaitė, and Toon Calders. 2013 · 2013
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Discrimination in online ad delivery
Latanya Sweeney. 2013 · 2013
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Learning fair representations. In
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. 2013 · 2013
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A multidisciplinary survey on discrimination analysis
Andrea Romei and Salvatore Ruggieri. 2014 · 2014
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On causal interpretation of race in regressions adjusting for confounding and mediating variables
Tyler J VanderWeele and Whitney R Robinson. 2014 · 2014
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Automated experiments on ad privacy settings: A tale of opacity, choice, and discrimination
Amit Datta, Michael Carl Tschantz, and Anupam Datta. 2015 · 2015
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Certifying and removing disparate impact. In
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. 2015 · 2015
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“Justice” and “fairness” are not the same thing
Barry Goldman and Russell Cropanzano. 2015 · 2015
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Deep learning and the information bottleneck principle. In
Naftali Tishby and Noga Zaslavsky. 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
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016 · 2016
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Big data’s disparate impact
Solon Barocas and Andrew D Selbst. 2016 · 2016
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016 · 2016
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Causally interpreting intersectionality theory
Liam Kofi Bright, Daniel Malinsky, and Morgan Thompson. 2016 · 2016
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COMPAS risk scales: Demonstrating accuracy equity and predictive parity
William Dieterich, Christina Mendoza, and Tim Brennan. 2016 · 2016
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Graphs for margins of Bayesian networks
Robin J Evans. 2016 · 2016
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A confidence-based approach for balancing fairness and accuracy. In
Benjamin Fish, Jeremy Kun, and Ádám D Lelkes. 2016 · 2016
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On the (im) possibility of fairness
Sorelle A Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. 2016 · 2016
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Equality of opportunity in supervised learning. In
Moritz Hardt, Eric Price, and Nati Srebro. 2016 · 2016
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Fair algorithms for infinite and contextual bandits
Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. 2016b · 2016
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Situation testing-based discrimination discovery: A causal inference approach. In
Lu Zhang, Yongkai Wu, and Xintao Wu. 2016 · 2016
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A convex framework for fair regression
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. 2017 · 2017
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova. 2017 · 2017
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Algorithmic decision making and the cost of fairness. In
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq. 2017 · 2017
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Algorithmic bias in autonomous systems. In
David Danks and Alex John London. 2017 · 2017
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Decision making with limited feedback: Error bounds for recidivism prediction and predictive policing
Danielle Ensign, Sorelle A Friedler, Scott Neville, Carlos Scheidegger, and Suresh Venkatasubramanian. 2017 · 2017
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Fairness in reinforcement learning. In
Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, and Aaron Roth. 2017 · 2017
Cited alongside, same era.
Meritocratic fairness for cross-population selection. In
Michael Kearns, Aaron Roth, and Zhiwei Steven Wu. 2017 · 2017
Cited alongside, same era.
Avoiding discrimination through causal reasoning. In
Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf. 2017 · 2017
Cited alongside, same era.
Inherent trade-offs in the fair determination of risk scores. In
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2017 · 2017
Cited alongside, same era.
Counterfactual fairness. In
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017 · 2017
Cited alongside, same era.
The hidden assumptions behind counterfactual explanations and principal reasons. In
Solon Barocas, Andrew D Selbst, and Manish Raghavan. 2020 · 2020
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Metric-free individual fairness in online learning. In
Yahav Bechavod, Christopher Jung, and Zhiwei Steven Wu. 2020 · 2020
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On the apparent conflict between individual and group fairness. In
Reuben Binns. 2020 · 2020
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Fairness in machine learning: A survey
Simon Caton and Christian Haas. 2020 · 2020
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A snapshot of the frontiers of fairness in machine learning
Alexandra Chouldechova and Aaron Roth. 2020 · 2020
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Multi-armed bandits with fairness constraints for distributing resources to human teammates. In
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Yang Liu, Goran Radanovic, Christos Dimitrakakis, Debmalya Mandal, and David C Parkes. 2017 · 2017
Cited alongside, same era.
Fair kernel learning. In
Adrián Pérez-Suay, Valero Laparra, Gonzalo Mateo-García, Jordi Muñoz-Marí, Luis Gómez-Chova, and Gustau Camps-Valls. 2017 · 2017
Cited alongside, same era.
Elements of Causal Inference: Foundations and Learning Algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. 2017 · 2017
Cited alongside, same era.
When worlds collide: Integrating different counterfactual assumptions in fairness. In
Chris Russell, Matt J Kusner, Joshua Loftus, and Ricardo Silva. 2017 · 2017
Cited alongside, same era.
Anti-discrimination learning: A causal modeling-based framework
Lu Zhang and Xintao Wu. 2017 · 2017
Cited alongside, same era.
A reductions approach to fair classification. In
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, and Hanna Wallach. 2018 · 2018
Cited alongside, same era.
Fairness in machine learning: Lessons from political philosophy. In
Reuben Binns. 2018 · 2018
Cited alongside, same era.
Houston Claure, Yifang Chen, Jignesh Modi, Malte Jung, and Stefanos Nikolaidis. 2020 · 2020
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Counterfactual risk assessments, evaluation, and fairness. In
Amanda Coston, Alan Mishler, Edward H Kennedy, and Alexandra Chouldechova. 2020 · 2020
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Causal modeling for fairness in dynamical systems. In
Elliot Creager, David Madras, Toniann Pitassi, and Richard Zemel. 2020 · 2020
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Fairness is not static: Deeper understanding of long term fairness via simulation studies. In
Alexander D’Amour, Hansa Srinivasan, James Atwood, Pallavi Baljekar, D Sculley, and Yoni Halpern. 2020 · 2020
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Algorithmic fairness from a non-ideal perspective. In
Sina Fazelpour and Zachary C Lipton. 2020 · 2020
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Verifying individual fairness in machine learning models. In
Philips George John, Deepak Vijaykeerthy, and Diptikalyan Saha. 2020 · 2020
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Causal Inference: What If
Miguel A Hernán and James M Robins. 2020 · 2020
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Fairness through equality of effort. In
Wen Huang, Yongkai Wu, Lu Zhang, and Xintao Wu. 2020 · 2020
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Metric learning for individual fairness. In
Christina Ilvento. 2020 · 2020
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Principal fairness for human and algorithmic decision-making
Kosuke Imai and Zhichao Jiang. 2020 · 2020
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How do classifiers induce agents to invest effort strategically?
Jon Kleinberg and Manish Raghavan. 2020 · 2020
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Hiring as exploration
Danielle Li, Lindsey R Raymond, and Peter Bergman. 2020 · 2020
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The disparate equilibria of algorithmic decision making when individuals invest rationally. In
Lydia T Liu, Ashia Wilson, Nika Haghtalab, Adam Tauman Kalai, Christian Borgs, and Jennifer Chayes. 2020 · 2020
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Survey on causal-based machine learning fairness notions
Karima Makhlouf, Sami Zhioua, and Catuscia Palamidessi. 2020 · 2020
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Two simple ways to learn individual fairness metrics from data. In
Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, and Yuekai Sun. 2020 · 2020
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Achieving equalized odds by resampling sensitive attributes
Yaniv Romano, Stephen Bates, and Emmanuel J Candès. 2020 · 2020
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Learning certified individually fair representations. In
Anian Ruoss, Mislav Balunovic, Marc Fischer, and Martin Vechev. 2020 · 2020
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Learning fair policies in multi-objective (deep) reinforcement learning with average and discounted rewards. In
Umer Siddique, Paul Weng, and Matthieu Zimmer. 2020 · 2020
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Robust optimization for fairness with noisy protected groups. In
Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, and Michael Jordan. 2020 · 2020
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Training individually fair ML models with sensitive subspace robustness. In
Mikhail Yurochkin, Amanda Bower, and Yuekai Sun. 2020 · 2020
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How do fair decisions fare in long-term qualification?. In
Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellstrom, Kun Zhang, and Cheng Zhang. 2020 · 2020
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Conditional learning of fair representations. In
Han Zhao, Amanda Coston, Tameem Adel, and Geoffrey J. Gordon. 2020 · 2020
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What we can’t measure, we can’t understand: challenges to demographic data procurement in the pursuit of fairness. In
McKane Andrus, Elena Spitzer, Jeffrey Brown, and Alice Xiang. 2021 · 2021
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Evaluating fairness of machine learning models under uncertain and incomplete information. In
Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, and Xuezhi Wang. 2021 · 2021
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Fairness in criminal justice risk assessments: The state of the art
Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth. 2021 · 2021
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Foundations of structural causal models with cycles and latent variables
Stephan Bongers, Patrick Forré, Jonas Peters, and Joris M Mooij. 2021 · 2021
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Model transferability with responsive decision subjects
Yatong Chen, Zeyu Tang, Yang Liu, and Kun Zhang. 2021 · 2021
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Fair mixup: Fairness via interpolation. In
Ching-Yao Chuang and Youssef Mroueh. 2021 · 2021
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Unfairness despite awareness: Group-fair classification with strategic agents
Andrew Estornell, Sanmay Das, Yang Liu, and Yevgeniy Vorobeychik. 2021 · 2021
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What’s fair about individual fairness?. In
Will Fleisher. 2021 · 2021
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Towards long-term fairness in recommendation. In
Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, and Yongfeng Zhang. 2021 · 2021
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Individual fairness in hindsight
Swati Gupta and Vijay Kamble. 2021 · 2021
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Allocating opportunities in a dynamic model of intergenerational mobility. In
Hoda Heidari and Jon Kleinberg. 2021 · 2021
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Constructing a fair classifier with generated fair data. In
Taeuk Jang, Feng Zheng, and Xiaoqian Wang. 2021 · 2021
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An algorithmic framework for fairness elicitation. In
Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, and Zhiwei Steven Wu. 2021 · 2021
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The use and misuse of counterfactuals in ethical machine learning. In
Atoosa Kasirzadeh and Andrew Smart. 2021 · 2021
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On the applicability of machine learning fairness notions
Karima Makhlouf, Sami Zhioua, and Catuscia Palamidessi. 2021 · 2021
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2021 · 2021
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Fairness in risk assessment instruments: Post-processing to achieve counterfactual equalized odds. In
Alan Mishler, Edward H Kennedy, and Alexandra Chouldechova. 2021 · 2021
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Achieving fairness in the stochastic multi-armed bandit problem
Vishakha Patil, Ganesh Ghalme, Vineet Nair, and Y Narahari. 2021 · 2021
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Unintended selection: Persistent qualification rate disparities and interventions
Reilly Raab and Yang Liu. 2021 · 2021
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Robust fairness under covariate shift. In
Ashkan Rezaei, Anqi Liu, Omid Memarrast, and Brian D Ziebart. 2021 · 2021
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Fairness violations and mitigation under covariate shift. In
Harvineet Singh, Rina Singh, Vishwali Mhasawade, and Rumi Chunara. 2021 · 2021
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Bandit learning with delayed impact of actions. In
Wei Tang, Chien-Ju Ho, and Yang Liu. 2021 · 2021
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Fairness of exposure in stochastic bandits. In
Lequn Wang, Yiwei Bai, Wen Sun, and Thorsten Joachims. 2021 · 2021
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Algorithms for fairness in sequential decision making. In
Min Wen, Osbert Bastani, and Ufuk Topcu. 2021 · 2021
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SenSeI: Sensitive set invariance for enforcing individual fairness. In
Mikhail Yurochkin and Yuekai Sun. 2021 · 2021
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Fairness in learning-based sequential decision algorithms: A survey
Xueru Zhang and Mingyan Liu. 2021 · 2021
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Learning fair policies in decentralized cooperative multi-agent reinforcement learning. In
Matthieu Zimmer, Claire Glanois, Umer Siddique, and Paul Weng. 2021 · 2021
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Demographic-reliant algorithmic fairness: Characterizing the risks of demographic data collection in the pursuit of fairness. In
McKane Andrus and Sarah Villeneuve. 2022 · 2022
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Why fair labels can yield unfair predictions: Graphical conditions for introduced unfairness
Carolyn Ashurst, Ryan Carey, Silvia Chiappa, and Tom Everitt. 2022 · 2022
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Individual fairness guarantees for neural networks. In
Elias Benussi, Andrea Patane, Matthew Wicker, L Laurenti, and Marta Kwiatkowska. 2022 · 2022
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Fairness transferability subject to bounded distribution shift. In
Yatong Chen, Reilly Raab, Jialu Wang, and Yang Liu. 2022 · 2022
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Improving fairness generalization through a sample-robust optimization method
Julien Ferry, Ulrich Aivodji, Sébastien Gambs, Marie-José Huguet, and Mohamed Siala. 2022 · 2022
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Are "intersectionally fair" AI algorithms really fair to women of color? A philosophical analysis. In
Youjin Kong. 2022 · 2022
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Causal conceptions of fairness and their consequences. In
Hamed Nilforoshan, Johann D Gaebler, Ravi Shroff, and Sharad Goel. 2022 · 2022
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A review on fairness in machine learning
Dana Pessach and Erez Shmueli. 2022 · 2022
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On the impossibility of non-trivial accuracy in presence of fairness constraints. In
Carlos Pinzón, Catuscia Palamidessi, Pablo Piantanida, and Frank Valencia. 2022 · 2022
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Attainability and optimality: The equalized odds fairness revisited. In
Zeyu Tang and Kun Zhang. 2022 · 2022
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On the fairness of causal algorithmic recourse. In
Julius von Kügelgen, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller, and Bernhard Schölkopf. 2022 · 2022
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Elizabeth Anne Watkins, Michael McKenna, and Jiahao Chen. 2022 · 2022
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Tier Balancing: Towards dynamic fairness over underlying causal factors. In
Zeyu Tang, Yatong Chen, Yang Liu, and Kun Zhang. 2023 · 2023
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