Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring

Bias in online freelance marketplaces: Evidence from taskrabbit and fiverr

Hannák, A.; Wagner, C.; Garcia, D.; Mislove, A.; Strohmaier, M.; and Wilson, C. 2017 · 1933

Earlier work this paper cites.

Sex effects on evaluation

Nieva, V. F.; and Gutek, B. A. 1980 · 1980

Earlier work this paper cites.

Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination

Bertrand, M.; and Mullainathan, S. 2004 · 2004

Earlier work this paper cites.

Controlling Fairness and Bias in Dynamic Learning-to-Rank

Original

Morik, M.; Singh, A.; Hong, J.; and Joachims, T. 2020 · 2005

Earlier work this paper cites.

Modeling result-list searching in the World Wide Web: The role of relevance topologies and trust bias

O’Brien, M.; and Keane, M. T. 2006 · 2006

Earlier work this paper cites.

Are people biased in their use of search engines?

Keane, M. T.; O’Brien, M.; and Smyth, B. 2008 · 2008

Earlier work this paper cites.

Exploring Artist Gender Bias in Music Recommendation

Original

Shakespeare, D.; Porcaro, L.; Gómez, E.; and Castillo, C. 2020 · 2009

Earlier work this paper cites.

Fairness through awareness

Dwork, C.; Hardt, M.; Pitassi, T.; Reingold, O.; and Zemel, R. 2012 · 2012

Earlier work this paper cites.

Click models for web search

Chuklin, A.; Markov, I.; and Rijke, M. d. 2015 · 2015

Earlier work this paper cites.

Unequal representation and gender stereotypes in image search results for occupations

Kay, M.; Matuszek, C.; and Munson, S. A. 2015 · 2015

Earlier work this paper cites.

Avoiding the south side and the suburbs: The geography of mobile crowdsourcing markets

Thebault-Spieker, J.; Terveen, L. G.; and Hecht, B. 2015 · 2015

Cited alongside, same era.

Pairwise interaction analysis of logistic regression models

Xu, E. L.; Qian, X.; Liu, T.; and Cui, S. 2016 · 2016

Cited alongside, same era.

Accurately interpreting clickthrough data as implicit feedback

Joachims, T.; Granka, L.; Pan, B.; Hembrooke, H.; and Gay, G. 2017 · 2017

Cited alongside, same era.

Fa* ir: A fair top-k ranking algorithm

Zehlike, M.; Bonchi, F.; Castillo, C.; Hajian, S.; Megahed, M.; and Baeza-Yates, R. 2017 · 2017

Cited alongside, same era.

Equity of attention: Amortizing individual fairness in rankings

Biega, A. J.; Gummadi, K. P.; and Weikum, G. 2018 · 2018

Cited alongside, same era.

Exploring author gender in book rating and recommendation

Ekstrand, M. D.; Tian, M.; Kazi, M. R. I.; Mehrpouyan, H.; and Kluver, D. 2018 · 2018

Gender differences in participation and reward on Stack Overflow

May, A.; Wachs, J.; and Hannák, A. 2019 · 2019

Later among the works it cites.

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Peng, A.; Nushi, B.; Kıcıman, E.; Inkpen, K.; Suri, S.; and Kamar, E. 2019 · 2019

Later among the works it cites.

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Singh, A.; and Joachims, T. 2019 · 2019

Later among the works it cites.

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Srivastava, M.; Heidari, H.; and Krause, A. 2019 · 2019

Later among the works it cites.

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