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Improved Boosted Classification to Mitigate the Ethnicity and Age Group Unfairness
Ivona Colakovic, Sašo Karakatič, 2022, objavljeni znanstveni prispevek na konferenci

Opis: 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.
Ključne besede: fairness, classification, boosting, machine learning
Objavljeno v DKUM: 02.08.2023; Ogledov: 346; Prenosov: 27
.pdf Celotno besedilo (884,95 KB)
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