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Recent work has raised concerns on the risk of unintended bias in AI systems being used nowadays that can affect individuals unfairly based on race, gender or religion, among other possible characteristics.
Discrimination-aware data mining
Dino Pedreshi, Salvatore Ruggieri, and Franco Turini · 2008
Earlier work this paper cites.
Building classifiers with independency constraints
Toon Calders, Faisal Kamiran, and Mykola Pechenizkiy · 2009
Earlier work this paper cites.
Three naive bayes approaches for discrimination-free classification
Toon Calders and Sicco Verwer · 2010
Earlier work this paper cites.
Fairness-aware learning through regularization approach
Toshihiro Kamishima, Shotaro Akaho, and Jun Sakuma · 2011
Earlier work this paper cites.
k-nn as an implementation of situation testing for discrimination discovery and prevention
Binh Thanh Luong, Salvatore Ruggieri, and Franco Turini · 2011
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
Earlier work this paper cites.
Why unbiased computational processes can lead to discriminative decision procedures
Toon Calders and Indrė Žliobaitė · 2013
Earlier work this paper cites.
Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
Earlier work this paper cites.
Forecasting: principles and practice:
R J Hyndman and G Athanasopoulos · 2014
Earlier work this paper cites.
Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
Earlier work this paper cites.
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2015
Cited alongside, same era.
On the relation between accuracy and fairness in binary classification
Indre Zliobaite · 2015
Cited alongside, same era.
Fairml : Toolbox for diagnosing bias in predictive modeling
J. A Adebayo · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
Cited alongside, same era.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2017
Later among the works it cites.
Fairness measures: Datasets and software for detecting algorithmic discrimination
Francesco Bonchi Sara Hajian Mohamed Megahed Meike Zehlike, Carlos Castillo · 2017
Later among the works it cites.
Fairtest: Discovering unwarranted associations in data-driven applications
Florian Tramer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, Jean-Pierre Hubaux, Mathias Humbert, Ari Juels, and Huang Lin · 2017
Later among the works it cites.
Rachel KE Bellamy, Kuntal Dey, Michael Hind, Samuel C Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, et al · 2018
Closest in time.
Gender shades: Intersectional accuracy disparities in commercial gender classification
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Fairness in learning: Classic and contextual bandits
Matthew Joseph, Michael Kearns, Jamie H Morgenstern, and Aaron Roth · 2016
Cited alongside, same era.
Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
Cited alongside, same era.
Algorithmic decision making and the cost of fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq · 2017
Cited alongside, same era.
Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi
Cited in the paper.
From parity to preference-based notions of fairness in classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Rodriguez, Krishna Gummadi, and Adrian Weller
Cited in the paper.
Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P Gummadi
Cited in the paper.
Joy Buolamwini and Timnit Gebru · 2018
Closest in time.
A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions
Alexandra Chouldechova, Diana Benavides-Prado, Oleksandr Fialko, and Rhema Vaithianathan · 2018
Closest in time.
A comparative study of fairness-enhancing interventions in machine learning
Sorelle A Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P Hamilton, and Derek Roth · 2018
Closest in time.
Reducing gender bias in google translate
James Kuczmarski · 2018
Closest in time.
Active fairness in algorithmic decision making
Alejandro Noriega-Campero, Michiel Bakker, Bernardo Garcia-Bulle, and Alex Pentland · 2018
Closest in time.