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In this work, we define and solve the Fair Top-k Ranking problem, in which we want to determine a subset of k candidates from a large pool of n >> k candidates, maximizing utility (i.e., select the "best" candidates) subject to group fairness criteria.
Bias in computer systems
Batya Friedman and Helen Nissenbaum. 1996 · 1996
Earlier work this paper cites.
The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proc. of SIGIR
Jaime Carbonell and Jade Goldstein. 1998 · 1998
Earlier work this paper cites.
Cumulated gain-based evaluation of IR techniques
Kalervo Järvelin and Jaana Kekäläinen. 2002 · 2002
Earlier work this paper cites.
Group rights and discrimination in international law
Nātān Lerner. 2003 · 2003
Earlier work this paper cites.
Affirmative action around the world: An empirical analysis
Thomas Sowell. 2005 · 2005
Earlier work this paper cites.
Discrimination-aware data mining. In Proc. of KDD
Dino Pedreshi, Salvatore Ruggieri, and Franco Turini. 2008 · 2008
Earlier work this paper cites.
Three naive Bayes approaches for discrimination-free classification
Toon Calders and Sicco Verwer. 2010 · 2010
Earlier work this paper cites.
Discrimination aware decision tree learning. In Proc. of ICDM
Faisal Kamiran, Toon Calders, and Mykola Pechenizkiy. 2010 · 2010
Earlier work this paper cites.
Gendering representation in Spain: Opportunities and limits of gender quotas
Tània Verge. 2010 · 2010
Earlier work this paper cites.
Evaluating diversified search results using per-intent graded relevance. In Proc. of SIGIR
Tetsuya Sakai and Ruihua Song. 2011 · 2011
Earlier work this paper cites.
Handling conditional discrimination. In Proc. of ICDM
Indre Žliobaite, Faisal Kamiran, and Toon Calders. 2011 · 2011
Earlier work this paper cites.
Fairness through awareness. In Proc. of ITCS
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012 · 2012
Earlier work this paper cites.
EU anti-discrimination law
Evelyn Ellis and Philippa Watson. 2012 · 2012
Earlier work this paper cites.
Fairness-aware classifier with prejudice remover regularizer
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2012 · 2012
Cited alongside, same era.
A methodology for direct and indirect discrimination prevention in data mining
Sara Hajian and Josep Domingo-Ferrer. 2013 · 2013
Cited alongside, same era.
UCI Machine Learning Repository
M. Lichman. 2013 · 2013
Cited alongside, same era.
Learning fair representations. In Proc. of ICML
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. 2013 · 2013
Cited alongside, same era.
Big data’s disparate impact
Solon Barocas and Andrew D Selbst. 2014 · 2014
Cited alongside, same era.
Generalization-based privacy preservation and discrimination prevention in data publishing and mining
Sara Hajian, Josep Domingo-Ferrer, and Oriol Farràs. 2014 · 2014
Cited alongside, same era.
Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining. In KDD Tutorials
Sara Hajian, Francesco Bonchi, and Carlos Castillo. 2016 · 2016
Later among the works it cites.
Equality of opportunity in supervised learning. In Proc. of NIPS
Moritz Hardt, Eric Price, and Nati Srebro. 2016 · 2016
Later among the works it cites.
Fair Learning in Markovian Environments
Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, and Aaron Roth. 2016 · 2016
Later among the works it cites.
Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2016 · 2016
Later among the works it cites.
Weapons of math destruction: How big data increases inequality and threatens democracy
Cathy O’Neil. 2016 · 2016
Later among the works it cites.
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SAT Percentile Ranks
The College Board. 2014 · 2014
Cited alongside, same era.
Certifying and removing disparate impact. In Proc. of KDD
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. 2015 · 2015
Cited alongside, same era.
Fairness constraints: A mechanism for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi. 2015 · 2015
Cited alongside, same era.
Machine Bias
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016 · 2016
Cited alongside, same era.
L Elisa Celis, Amit Deshpande, Tarun Kathuria, and Nisheeth K Vishnoi. 2016 · 2016
Cited alongside, same era.
On the (im) possibility of fairness
Sorelle A Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. 2016 · 2016
Cited alongside, same era.
Measuring Fairness in Ranked Outputs. In Proc. of FATML
Ke Yang and Julia Stoyanovich. 2016 · 2016
Later among the works it cites.
Exposing the probabilistic causal structure of discrimination
Francesco Bonchi, Sara Hajian, Bud Mishra, and Daniele Ramazzotti. 2017 · 2017
Closest in time.
Ranking with Fairness Constraints
L Elisa Celis, Damian Straszak, and Nisheeth K Vishnoi. 2017 · 2017
Closest in time.
Recommender Response to Diversity and Popularity Bias in User Profiles (short paper). In 30th International Florida Artificial Intelligence Research Society Conference
Sushma Channamsetty and Michael D Ekstrand. 2017 · 2017
Closest in time.
Algorithmic decision making and the cost of fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq. 2017 · 2017
Closest in time.
Quantifying search bias: Investigating sources of bias for political searches in social media. In Proc. of CSCW
Juhi Kulshrestha, Muhammad B. Zafar, Motahhare Eslami, Saptarshi Ghosh, Johnnatan Messias, and Krishna P. Gummadi. 2017 · 2017
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Diversity in recommender systems–A survey
Matevž Kunaver and Tomaž Požrl. 2017 · 2017
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