20.500.12556/DKUM-84874 Improved Boosted Classification to Mitigate the Ethnicity and Age Group Unfairness 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. fairness classification boosting machine learning klasifikacija strojno učenje pravičnost true false false Angleški jezik Slovenski jezik Znanstveno delo 2023-08-02 14:58:02 2023-08-02 14:58:03 2023-08-10 03:07:22 0000-00-00 00:00:00 2022 0 0 Str. 432-437 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.6 119158531 142225667 10.5220/0011287400003269 Improved_Boosted_Classification-Colakovic-2022.pdf Improved_Boosted_Classification-Colakovic-2022.pdf 1 634445BD07B9754578C90B5044514B53 a59f0dfaa5e90a408c5f5ad213df080525e01c06f025d3d094bd87bb9aeecde1 0ab34716-36a1-11ee-b829-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=169330 https://www.scitepress.org/Link.aspx?doi=10.5220/0011287400003269 0 aad07dfc-3134-11ee-b6b4-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=169179 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0