Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/42954
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DC Field | Value | Language |
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dc.contributor.author | Grau, Isel | - |
dc.contributor.author | NAPOLES RUIZ, Gonzalo | - |
dc.contributor.author | Hoitsma, Fabian | - |
dc.contributor.author | KOUTSOVITI-KOUMERI, Lisa | - |
dc.contributor.author | VANHOOF, Koen | - |
dc.date.accessioned | 2024-05-15T09:09:43Z | - |
dc.date.available | 2024-05-15T09:09:43Z | - |
dc.date.issued | 2024 | - |
dc.date.submitted | 2024-04-26T12:25:14Z | - |
dc.identifier.citation | Springer, p. 745 -764 | - |
dc.identifier.isbn | 978-3-031-47720-1 | - |
dc.identifier.isbn | 978-3-031-47721-8 | - |
dc.identifier.issn | 2367-3370 | - |
dc.identifier.issn | 2367-3389 | - |
dc.identifier.uri | http://hdl.handle.net/1942/42954 | - |
dc.description.abstract | In this paper, we integrate the concepts of feature importance with implicit bias in the context of pattern classification. This is done by means of a three-step methodology that involves (i) building a classifier and tuning its hyperparameters, (ii) building a Fuzzy Cognitive Map model able to quantify implicit bias, and (iii) using the SHAP feature importance to active the neural concepts when performing simulations. The results using a real case study concerning fairness research support our two-fold hypothesis. On the one hand, it is illustrated the risks of using a feature importance method as an absolute tool to measure implicit bias. On the other hand, it is concluded that the amount of bias towards protected features might differ depending on whether the features are numerically or categorically encoded. | - |
dc.language.iso | en | - |
dc.publisher | Springer | - |
dc.relation.ispartofseries | Lecture Notes in Networks and Systems | - |
dc.subject.other | Fairness | - |
dc.subject.other | Implicit Bias | - |
dc.subject.other | Explainable artificial intelligence | - |
dc.subject.other | Feature importance | - |
dc.subject.other | Fuzzy cognitive maps | - |
dc.title | Measuring Implicit Bias Using SHAP Feature Importance and Fuzzy Cognitive Maps | - |
dc.type | Proceedings Paper | - |
local.bibliographicCitation.conferencedate | 7-8 September 2023 | - |
local.bibliographicCitation.conferencename | Intelligent Systems Conference IntelliSys 2023 | - |
local.bibliographicCitation.conferenceplace | Amsterdam, the Netherlands | - |
dc.identifier.epage | 764 | - |
dc.identifier.spage | 745 | - |
dc.identifier.volume | 822 | - |
local.bibliographicCitation.jcat | C1 | - |
local.type.refereed | Refereed | - |
local.type.specified | Proceedings Paper | - |
dc.identifier.doi | 10.1007/978-3-031-47721-8_50 | - |
dc.identifier.isi | 001261691200050 | - |
dc.identifier.eissn | 2367-3389 | - |
local.provider.type | CrossRef | - |
local.bibliographicCitation.btitle | Lecture Notes in Networks and Systems | - |
local.dataset.doi | 10.24432/C5NC77 | - |
local.uhasselt.international | yes | - |
item.fullcitation | Grau, Isel; NAPOLES RUIZ, Gonzalo; Hoitsma, Fabian; KOUTSOVITI-KOUMERI, Lisa & VANHOOF, Koen (2024) Measuring Implicit Bias Using SHAP Feature Importance and Fuzzy Cognitive Maps. In: Springer, p. 745 -764. | - |
item.fulltext | With Fulltext | - |
item.contributor | Grau, Isel | - |
item.contributor | NAPOLES RUIZ, Gonzalo | - |
item.contributor | Hoitsma, Fabian | - |
item.contributor | KOUTSOVITI-KOUMERI, Lisa | - |
item.contributor | VANHOOF, Koen | - |
item.accessRights | Restricted Access | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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Pages from 978-3-031-47721-8.pdf Restricted Access | Published version | 775.59 kB | Adobe PDF | View/Open Request a copy |
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