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http://hdl.handle.net/1942/36899
Title: | Bias Quantification for Protected Features in Pattern Classification Problems | Authors: | KOUTSOVITI-KOUMERI, Lisa NAPOLES RUIZ, Gonzalo |
Issue Date: | 2021 | Publisher: | Springer | Source: | Tavares, João Manuel R. S.; Papa, João Paulo; Hidalgo, Manuel González (Ed.). Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications 25th Iberoamerican Congress, CIARP 2021, Porto, Portugal, May 10–13, 2021, Revised Selected Papers, Springer, p. 351 -360 | Series/Report no.: | 12702 | Abstract: | The need to measure and mitigate bias in machine learning data sets has gained wide recognition in the field of Artificial Intelligence (AI) during the past decade. The academic and business communities call for new general-purpose measures to quantify bias. In this paper, we propose a new measure that relies on the fuzzy-rough set theory. The intuition of our measure is that protected features should not change the fuzzy-rough set boundary regions significantly. The extent to which this happens can be understood as a proxy for bias quantification. Our measure can be categorized as an individual fairness measure since the fuzzy-rough regions are computed using instance-based information pieces. The main advantage of our measure is that it does not depend on any prediction model but on a distance function. At the same time, our measure offers an intuitive rationale for the bias concept. The results using a proof-of-concept show that our measure can capture the bias issues better than other state-of-the-art measures. | Keywords: | Fuzzy-rough sets;Fairness-aware AI;Bias | Document URI: | http://hdl.handle.net/1942/36899 | ISBN: | 9783030934200 9783030934194 |
DOI: | 10.1007/978-3-030-93420-0_33 | Category: | C1 | Type: | Proceedings Paper | Validations: | vabb 2023 |
Appears in Collections: | Research publications |
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Pages from 2021_Book_ProgressInPatternRecognitionIm.pdf Restricted Access | Published version | 668.36 kB | Adobe PDF | View/Open Request a copy |
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