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Algorithmic fairness in decision-making has been studied extensively in static settings where one-shot decisions are made on tasks such as classification.
Finite-time analysis of the multiarmed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer · 2002
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Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2012
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Why unbiased computational processes can lead to discriminative decision procedures
Toon Calders and Indrė Žliobaitė · 2013
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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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
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
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Fairness in learning: Classic and contextual bandits
Matthew Joseph, Michael Kearns, Jamie H Morgenstern, and Aaron Roth · 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
Earlier work this paper cites.
Fairness in reinforcement learning
Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, and Aaron Roth · 2017
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Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
Earlier work this paper cites.
Calibrated fairness in bandits
Yang Liu, Goran Radanovic, Christos Dimitrakakis, Debmalya Mandal, and David C Parkes · 2017
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No classification without representation: Assessing geodiversity issues in open data sets for the developing world
Shreya Shankar, Yoni Halpern, Eric Breck, James Atwood, Jimbo Wilson, and D Sculley · 2017
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2017
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A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, and Hanna Wallach · 2018
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On preserving non-discrimination when combining expert advice
Avrim Blum, Suriya Gunasekar, Thodoris Lykouris, and Nati Srebro · 2018
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How algorithmic confounding in recommendation systems increases homogeneity and decreases utility
Allison JB Chaney, Brandon M Stewart, and Barbara E Engelhardt · 2018
Cited alongside, same era.
The accuracy, fairness, and limits of predicting recidivism
Julia Dressel and Hany Farid · 2018
Cited alongside, same era.
Runaway feedback loops in predictive policing
Danielle Ensign, Sorelle A Friedler, Scott Neville, Carlos Scheidegger, and Suresh Venkatasubramanian · 2018
Cited alongside, same era.
Predictably unequal? the effects of machine learning on credit markets
Andreas Fuster, Paul Goldsmith-Pinkham, Tarun Ramadorai, and Ansgar Walther · 2018
Cited alongside, same era.
Online learning with an unknown fairness metric
Stephen Gillen, Christopher Jung, Michael Kearns, and Aaron Roth · 2018
Cited alongside, same era.
Amazon’s alexa and google home show accent bias, with chinese and spanish hardest to understand
Obtaining fairness using optimal transport theory
Paula Gordaliza, Eustasio Del Barrio, Gamboa Fabrice, and Loubes Jean-Michel · 2019
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Individual fairness in hindsight
Swati Gupta and Vijay Kamble · 2019
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On the long-term impact of algorithmic decision policies: Effort unfairness and feature segregation through social learning
Hoda Heidari, Vedant Nanda, and Krishna Gummadi · 2019
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Downstream effects of affirmative action
Sampath Kannan, Aaron Roth, and Juba Ziani · 2019
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ifair: Learning individually fair data representations for algorithmic decision making
Preethi Lahoti, Krishna P Gummadi, and Gerhard Weikum · 2019
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Combinatorial sleeping bandits with fairness constraints
Fengjiao Li, Jia Liu, and Bo Ji · 2019
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Drew Harwell · 2018
Cited alongside, same era.
Fairness without demographics in repeated loss minimization
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang · 2018
Cited alongside, same era.
Preventing disparate treatment in sequential decision making
Hoda Heidari and Andreas Krause · 2018
Cited alongside, same era.
A short-term intervention for long-term fairness in the labor market
Lily Hu and Yiling Chen · 2018
Cited alongside, same era.
Meritocratic fairness for infinite and contextual bandits
Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth · 2018
Cited alongside, same era.
Delayed impact of fair machine learning
Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, and Moritz Hardt · 2018
Cited alongside, same era.
Enhancing the accuracy and fairness of human decision making
I. Valera, A. Singla, and M. Gomez Rodriguez · 2018
Cited alongside, same era.
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The disparate equilibria of algorithmic decision making when individuals invest rationally
Lydia T Liu, Ashia Wilson, Nika Haghtalab, Adam Tauman Kalai, Christian Borgs, and Jennifer Chayes · 2019
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2019
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From fair decision making to social equality
Hussein Mouzannar, Mesrob I Ohannessian, and Nathan Srebro · 2019
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Dissecting racial bias in an algorithm that guides health decisions for 70 million people
Ziad Obermeyer and Sendhil Mullainathan · 2019
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Achieving fairness in the stochastic multi-armed bandit problem
Vishakha Patil, Ganesh Ghalme, Vineet Nair, and Y Narahari · 2019
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Min Wen, Osbert Bastani, and Ufuk Topcu · 2019
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Fairness constraints: A flexible approach for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P. Gummadi · 2019
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Long-term impacts of fair machine learning
Xueru Zhang, Mohammad Mahdi Khalili, and Mingyan Liu · 2019
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Xueru Zhang, Mohammad Mahdi Khalili, Cem Tekin, and Mingyan Liu · 2019
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