Name
Abstract | ||
|---|---|---|
In recent years, it has been revealed that machine learning models can produce discriminatory predictions. Hence, fairness protection has come to play a pivotal role in machine learning. In the past, most studies on fairness protection have used traditional machine learning methods to enforce fairness. However, these studies focus on low dimensional inputs, such as numerical inputs, whereas more recent deep learning technologies have encouraged fairness protection with image inputs through deep model methods. These approaches involve various object functions and structural designs that break the spurious correlations between targets and sensitive features. With these connections broken, we are left with fairer predictions. To better understand the proposed methods and encourage further development in the field, this paper summarizes fairness protection methods in terms of three aspects: the problem settings, the models, and the challenges. Through this survey, we hope to reveal research trends in the field, discover the fundamentals of enforcing fairness, and summarize the main challenges to producing fairer models. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s00521-022-07136-1 | Neural Computing and Applications |
Keywords | DocType | Volume |
Image fairness protection, Deep learning, Fair representations | Journal | 34 |
Issue | ISSN | Citations |
15 | 0941-0643 | 0 |
PageRank | References | Authors |
0.34 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huan Tian | 1 | 0 | 0.34 |
| Tianqing Zhu | 2 | 1 | 1.04 |
| Wei Liu | 3 | 468 | 37.36 |
| Wanlei Zhou | 4 | 2295 | 189.31 |