Adaptive boosting method for mitigating ethnicity and age group unfairness

Ivona Čolaković (Author), Sašo Karakatič (Author)

Abstract

Machine learning algorithms make decisions in various fields, thus influencing people’s lives. However, despite their good quality, they can be unfair to certain demographic groups, perpetuating socially induced biases. Therefore, this paper deals with a common unfairness problem, unequal quality of service, that appears in classification when age and ethnicity groups are used. To tackle this issue, we propose an adaptive boosting algorithm that aims to mitigate the existing unfairness in data. The proposed method is based on the AdaBoost algorithm but incorporates fairness in the calculation of the instance’s weight with the goal of making the prediction as good as possible for all ages and ethnicities. The results show that the proposed method increases the fairness of age and ethnicity groups while maintaining good overall quality compared to traditional classification algorithms. The proposed method achieves the best accuracy in almost every sensitive feature group. Based on the extensive analysis of the results, we found that when it comes to ethnicity, interestingly, White people are likely to be incorrectly classified as not being heroin users, whereas other groups are likely to be incorrectly classified as heroin users.

Keywords

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

Data

Language:	English
Year of publishing:	2024
Typology:	1.01 - Original Scientific Article
Organization:	UM FERI - Faculty of Electrical Engineering and Computer Science
Publisher:	Springer Nature
UDC:	004.8
COBISS:	172430083
ISSN:	2661-8907
Views:	42
Downloads:	0
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	pravičnost;strojno učenje;klasifikacija;
Type (COBISS):	Article
Pages:	9 str.
Volume:	ǂVol. ǂ5
Issue:	article no. 10
Chronology:	2024
DOI:	10.1007/s42979-023-02342-7
ID:	23784101