Improved Boosted Classification to Mitigate the Ethnicity and Age Group Unfairness

Ivona Colakovic, Sašo Karakatič

2022

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.

Paper Citation

in Harvard Style

Colakovic I. and Karakatič S. (2022). Improved Boosted Classification to Mitigate the Ethnicity and Age Group Unfairness. In Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-583-8, pages 432-437. DOI: 10.5220/0011287400003269

in Bibtex Style

@conference{data22,
author={Ivona Colakovic and Sašo Karakatič},
title={Improved Boosted Classification to Mitigate the Ethnicity and Age Group Unfairness},
booktitle={Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2022},
pages={432-437},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011287400003269},
isbn={978-989-758-583-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Improved Boosted Classification to Mitigate the Ethnicity and Age Group Unfairness
SN - 978-989-758-583-8
AU - Colakovic I.
AU - Karakatič S.
PY - 2022
SP - 432
EP - 437
DO - 10.5220/0011287400003269