| Title: | Adaptive boosting method for mitigating ethnicity and age group unfairness |
|---|
| Authors: | ID Colakovic, Ivona (Author)
ID Karakatič, Sašo (Author) |
|---|
| Files: |  s42979-023-02342-7.pdf (1,66 MB)
MD5: D69BBDAE109AEDCF92A3DFE01958F4BB
 https://link.springer.com/article/10.1007/s42979-023-02342-7
|
|---|
| Language: | English |
|---|
| Work type: | Article |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
|
|---|
| 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: | fairness, boosting, machine learning, classification |
|---|
| Publication status: | Published |
|---|
| Publication version: | Version of Record |
|---|
| Submitted for review: | 05.11.2022 |
|---|
| Article acceptance date: | 20.09.2023 |
|---|
| Publication date: | 15.11.2023 |
|---|
| Publisher: | Springer Nature |
|---|
| Year of publishing: | 2024 |
|---|
| Number of pages: | 9 str. |
|---|
| Numbering: | Vol. 5, article no. 10 |
|---|
| PID: | 20.500.12556/DKUM-88791  |
|---|
| UDC: | 004.8 |
|---|
| ISSN on article: | 2661-8907 |
|---|
| COBISS.SI-ID: | 172430083 |
|---|
| DOI: | 10.1007/s42979-023-02342-7 |
|---|
| Copyright: | © The Author(s) 2023 |
|---|
| Publication date in DKUM: | 24.05.2024 |
|---|
| Views: | 599 |
|---|
| Downloads: | 27 |
|---|
| Metadata: |    |
|---|
| Categories: | Misc.
|
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
