Improved Boosted Classification to Mitigate the Ethnicity and Age Group Unfairness

Ivona Čolaković (Author), Sašo Karakatič (Author)

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

klasifikacija;strojno učenje;pravičnost;fairness;classification;boosting;machine learning;

Data

Language:	English
Year of publishing:	2022
Typology:	1.08 - Published Scientific Conference Contribution
Organization:	UM FERI - Faculty of Electrical Engineering and Computer Science
UDC:	004.6
COBISS:	142225667
Views:	74
Downloads:	6
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	klasifikacija;strojno učenje;pravičnost;
Type (COBISS):	Scientific work
Pages:	Str. 432-437
DOI:	10.5220/0011287400003269
ID:	19700263