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Title:Improved Boosted Classification to Mitigate the Ethnicity and Age Group Unfairness
Authors:ID Colakovic, Ivona (Author)
ID Karakatič, Sašo (Author)
Files:.pdf Improved_Boosted_Classification-Colakovic-2022.pdf (884,95 KB)
MD5: 634445BD07B9754578C90B5044514B53
URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011287400003269
Work type:Scientific work
Typology:1.08 - Published Scientific Conference Contribution
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:This paper deals with the group fairness issue that arises when classifying data, which contains socially induced biases for age and ethnicity. To tackle the unfair focus on certain age and ethnicity groups, we propose an adaptive boosting method that balances the fair treatment of all groups. The proposed approach builds upon the AdaBoost method but supplements it with the factor of fairness between the sensitive groups. The results show that the proposed method focuses more on the age and ethnicity groups, given less focus with traditional classification techniques. Thus the resulting classification model is more balanced, treating all of the sensitive groups more equally without sacrificing the overall quality of the classification.
Keywords:fairness, classification, boosting, machine learning
Year of publishing:2022
Number of pages:Str. 432-437
PID:20.500.12556/DKUM-84874 New window
COBISS.SI-ID:142225667 New window
DOI:10.5220/0011287400003269 New window
Publication date in DKUM:02.08.2023
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Record is a part of a monograph

Title:Proceedings of the 11th International Conference on Data Science, Technology and Applications : July 11-13, 2022, in Lisbon, Portugal
Editors:Alfredo Cuzzocrea
Place of publishing:Setúbal
Publisher:Science and Technology Publications
Year of publishing:2022
COBISS.SI-ID:119158531 New window
Collection title:DATA International Conference on DATA Management Technologies and Applications
Collection ISSN:2184-285X

Document is financed by a project

Funder:ARRS - Slovenian Research Agency
Project number:P2-0057
Name:Informacijski sistemi


License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

Secondary language

Keywords:klasifikacija, strojno učenje, pravičnost


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