Improved Boosted Classification to Mitigate the Ethnicity and Age Group Unfairness

Ivona Čolaković (Avtor), Sašo Karakatič (Avtor)

Povzetek

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

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

Podatki

Jezik:	Angleški jezik
Leto izida:	2022
Tipologija:	1.08 - Objavljeni znanstveni prispevek na konferenci
Organizacija:	UM FERI - Fakulteta za elektrotehniko, računalništvo in informatiko
UDK:	004.6
COBISS:	142225667
Št. ogledov:	74
Št. prenosov:	6
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Slovenski jezik
Sekundarne ključne besede:	klasifikacija;strojno učenje;pravičnost;
Vrsta dela (COBISS):	Znanstveno delo
Strani:	Str. 432-437
DOI:	10.5220/0011287400003269
ID:	19700263