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2018

Synthetic Attribute Data for Evaluating Consumer-side Fairness

Burke, Robin, Kontny, Jackson, Sonboli, Nasim

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When evaluating recommender systems for their fairness, it may be necessary to make use of demographic attributes, which are personally sensitive and usually excluded from publicly-available data sets.

In addition, these attributes are fixed and therefore it is not possible to experiment with different distributions using the same data.
In this paper, we describe the Frequency-Linked Attribute Generation (FLAG) algorithm, and show its applicability for assigning synthetic demographic attributes to recommendation data sets.

Synthetic Attribute Data for Evaluating Consumer-side Fairness · Around

Built on

Individuals without jobs: An empirical study of job-seeking behavior and reemployment
Connie R Wanberg, John D Watt, and Deborah J Rumsey. 1996 · 1996
Earlier work this paper cites.
Gender differences in anticipated salary: Role of salary estimates for others, job characteristics, career paths, and job inputs
Teresa M Heckert, Heather E Droste, Patrick J Adams, Christopher M Griffin, Lisa L Roberts, Michael A Mueller, and Hope A Wallis. 2002 · 2002
Earlier work this paper cites.
Racial differences in perceptions of starting salaries: How failing to discriminate can perpetuate discrimination
Derek R Avery. 2003 · 2003
Earlier work this paper cites.

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