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Title: DENOUNCER: detection of unfairness in classifiers
The use of automated data-driven tools for decision-making has gained popularity in recent years. At the same time, the reported cases of algorithmic bias and discrimination increase as well, which in turn lead to an extensive study of algorithmic fairness. Numerous notions of fairness have been proposed, designed to capture different scenarios. These measures typically refer to a "protected group" in the data, defined using values of some sensitive attributes. Confirming whether a fairness definition holds for a given group is a simple task, but detecting groups that are treated unfairly by the algorithm may be computationally prohibitive as the number of possible groups is combinatorial. We present a method for detecting such groups efficiently for various fairness definitions. Our solution is implemented in a system called DENOUNCER, an interactive system that allows users to explore different fairness measures of a (trained) classifier for a given test data. We propose to demonstrate the usefulness of DENOUNCER using real-life data and illustrate the effectiveness of our method.  more » « less
Award ID(s):
1741022 1934565 2106176
PAR ID:
10353854
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the VLDB Endowment
Volume:
14
Issue:
12
ISSN:
2150-8097
Page Range / eLocation ID:
2719 to 2722
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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