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Fairness is a concept of justice.
Fairness Through Awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Rich Zemel · 2011
Earlier work this paper cites.
Charter of fundamental rights of the european union
Council of Europe · 2012
Earlier work this paper cites.
Quantifying explainable discrimination and removing illegal discrimination in automated decision making
F. Kamiran, I. Zliobaite, and T.G.K. Calders · 2013
Earlier work this paper cites.
A primer on fairness in criminal justice risk assessments
Richard Berk · 2016
Earlier work this paper cites.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2016
Earlier work this paper cites.
Assessing calibration of prognostic risk scores
Cynthia Crowson, Elizabeth J. Atkinson, Terry M Therneau, Andrew B. Lawson, Duncan Lee, and Ying MacNab · 2016
Earlier work this paper cites.
Equality of Opportunity in Supervised Learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
Earlier work this paper cites.
Inherent trade-offs in the fair determination of risk scores
Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
Cited alongside, same era.
Machine bias. ProPublica, May 23, 2016, 2016
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
Cited alongside, same era.
Compas risk scales: Demonstrating accuracy equity and predictive parity
William Dieterich, Christina Mendoza, and Tim Brennan · 2016
Cited alongside, same era.
On the (im)possibility of fairness
Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian · 2016
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 in criminal justice risk assessments: The state of the art
The measure and mismeasure of fairness: A critical review of fair machine learning
Sam Corbett-Davies and Sharad Goel · 2018
Later among the works it cites.
Aequitas: A bias and fairness audit toolkit
Pedro Saleiro, Benedict Kuester, Abby Stevens, Ari Anisfeld, Loren Hinkson, Jesse London, and Rayid Ghani · 2018
Later among the works it cites.
A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2019
Later among the works it cites.
Fairness and Machine Learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2019
Later among the works it cites.
Unlocking fairness: a trade-off revisited
Michael Wick, Swetasudha Panda, and Jean-Baptiste Tristan · 2019
Later among the works it cites.
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Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth · 2017
Cited alongside, same era.
Big data’s disparate impact
Solon Barocas and Andrew D Selbst
Cited in the paper.
Identifying and correcting label bias in machine learning
Heinrich Jiang and Ofir Nachum · 2019
Later among the works it cites.