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We present a framework for quantifying and mitigating algorithmic bias in mechanisms designed for ranking individuals, typically used as part of web-scale search and recommendation systems.
On information and sufficiency
S. Kullback and R. A. Leibler · 1951
Earlier work this paper cites.
Bias in computer systems
B. Friedman and H. Nissenbaum · 1996
Earlier work this paper cites.
Cumulated gain-based evaluation of IR techniques
K. Jarvelin and J. Kekalainen · 2002
Earlier work this paper cites.
Accurately interpreting clickthrough data as implicit feedback
T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay · 2005
Earlier work this paper cites.
Discrimination-aware data mining
D. Pedreschi, S. Ruggieri, and F. Turini · 2008
Earlier work this paper cites.
Measuring discrimination in socially-sensitive decision records
D. Pedreschi, S. Ruggieri, and F. Turini · 2009
Earlier work this paper cites.
Three naive bayes approaches for discrimination-free classification
T. Calders and S. Verwer · 2010
Earlier work this paper cites.
Discrimination aware decision tree learning
F. Kamiran, T. Calders, and M. Pechenizkiy · 2010
Earlier work this paper cites.
Gendering representation in Spain: Opportunities and limits of gender quotas
T. Verge · 2010
Earlier work this paper cites.
Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, and R. Z. Omer Reingold · 2012
Earlier work this paper cites.
A methodology for direct and indirect discrimination prevention in data mining
S. Hajian and J. Domingo-Ferrer · 2013
Earlier work this paper cites.
Learning fair representations
R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
Earlier work this paper cites.
Generalization-based privacy preservation and discrimination prevention in data publishing and mining
S. Hajian, J. Domingo-Ferrer, and O. Farràs · 2014
Earlier work this paper cites.
Did you mean “Galene”?, 2014
S. Sankar and A. Makhani · 2014
Earlier work this paper cites.
Unequal representation and gender stereotypes in image search results for occupations
M. Kay, C. Matuszek, and S. A. Munson · 2015
Earlier work this paper cites.
Machine bias
J. Angwin, J. Larson, S. Mattu, and L. Kirchner · 2016
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Big data’s disparate impact
S. Barocas and A. D. Selbst · 2016
Cited alongside, same era.
Man is to computer programmer as woman is to homemaker? Debiasing word embeddings
T. Bolukbasi, K.-W. Chang, J. Y. Zou, V. Saligrama, and A. T. Kalai · 2016
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How to be fair and diverse?
L. E. Celis, A. Deshpande, T. Kathuria, and N. K. Vishnoi · 2016
Cited alongside, same era.
On the (im) possibility of fairness
S. A. Friedler, C. Scheidegger, and S. Venkatasubramanian · 2016
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Algorithmic bias: From discrimination discovery to fairness-aware data mining
S. Hajian, F. Bonchi, and C. Castillo · 2016
Cited alongside, same era.
Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
M. B. Zafar, I. Valera, M. Gomez Rodriguez, and K. P. Gummadi · 2017
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FA*IR: A fair top-k ranking algorithm
M. Zehlike, F. Bonchi, C. Castillo, S. Hajian, M. Megahed, and R. Baeza-Yates · 2017
Later among the works it cites.
Measuring discrimination in algorithmic decision making
I. Žliobaitė · 2017
Later among the works it cites.
https://ec.europa.eu/info/strategy/justice-and-fundamental-rights/discrimination/tackling-discrimination/diversity-management/diversity-charters_en
European commission diversity charters, 2018 · 2018
Later among the works it cites.
Four conceptions of equal opportunity
R. Arneson · 2018
Later among the works it cites.
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Equality of opportunity in supervised learning
M. Hardt, E. Price, and N. Srebro · 2016
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https://www1.eeoc.gov/eeoc/newsroom/release/1-3-17.cfm
U.S. equal employment opportunity commission, 2017 · 2017
Cited alongside, same era.
Semantics derived automatically from language corpora contain human-like biases
A. Caliskan, J. J. Bryson, and A. Narayanan · 2017
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Optimized pre-processing for discrimination prevention
F. Calmon, D. Wei, B. Vinzamuri, K. N. Ramamurthy, and K. R. Varshney · 2017
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Algorithmic decision making and the cost of fairness
S. Corbett-Davies, E. Pierson, A. Feller, S. Goel, and A. Huq · 2017
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Fairness in reinforcement learning
S. Jabbari, M. Joseph, M. Kearns, J. Morgenstern, and A. Roth · 2017
Cited alongside, same era.
Equity of attention: Amortizing individual fairness in rankings
A. J. Biega, K. P. Gummadi, and G. Weikum · 2018
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Fairness and transparency in ranking
C. Castillo · 2018
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Ranking with fairness constraints
L. E. Celis, D. Straszak, and N. K. Vishnoi · 2018
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Talent search and recommendation systems at LinkedIn: Practical challenges and lessons learned
S. C. Geyik, Q. Guo, B. Hu, C. Ozcaglar, K. Thakkar, X. Wu, and K. Kenthapadi · 2018
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Building representative talent search at LinkedIn, 2018
S. C. Geyik and K. Kenthapadi · 2018
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Fairness of exposure in rankings
A. Singh and T. Joachims · 2018
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Designing fair ranking schemes
A. Asudeh, H. V. Jagadish, J. Stoyanovich, and G. Das · 2019
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Tutorial: Fairness-aware machine learning: Practical challenges and lessons learned
S. Bird, B. Hutchinson, K. Kenthapadi, E. Kiciman, and M. Mitchell · 2019
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A comparative study of fairness-enhancing interventions in machine learning
S. A. Friedler, C. Scheidegger, S. Venkatasubramanian, S. Choudhary, E. P. Hamilton, and D. Roth · 2019
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