dc.identifier.uri |
http://dx.doi.org/10.15488/14664 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/14782 |
|
dc.contributor.author |
Le Quy, Tai
|
|
dc.contributor.author |
Roy, Arjun
|
|
dc.contributor.author |
Iosifidis, Vasileios
|
|
dc.contributor.author |
Zhang, Wenbin
|
|
dc.contributor.author |
Ntoutsi, Eirini
|
|
dc.date.accessioned |
2023-09-01T06:38:31Z |
|
dc.date.available |
2023-09-01T06:38:31Z |
|
dc.date.issued |
2022 |
|
dc.identifier.citation |
Le Quy, T.; Roy, A.; Iosifidis, V.; Zhang, W.; Ntoutsi, E.: A survey on datasets for fairness-aware machine learning. In: Wiley Interdisciplinary Reviews (WIREs). Data Mining and Knowledge Discovery 12 (2022), Nr. 3, e1452. DOI: https://doi.org/10.1002/widm.1452 |
|
dc.description.abstract |
As decision-making increasingly relies on machine learning (ML) and (big) data, the issue of fairness in data-driven artificial intelligence systems is receiving increasing attention from both research and industry. A large variety of fairness-aware ML solutions have been proposed which involve fairness-related interventions in the data, learning algorithms, and/or model outputs. However, a vital part of proposing new approaches is evaluating them empirically on benchmark datasets that represent realistic and diverse settings. Therefore, in this paper, we overview real-world datasets used for fairness-aware ML. We focus on tabular data as the most common data representation for fairness-aware ML. We start our analysis by identifying relationships between the different attributes, particularly with respect to protected attributes and class attribute, using a Bayesian network. For a deeper understanding of bias in the datasets, we investigate interesting relationships using exploratory analysis. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Fundamental Concepts of Data and Knowledge > Data Concepts Technologies > Data Preprocessing. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Hoboken, NJ : Wiley |
|
dc.relation.ispartofseries |
Wiley Interdisciplinary Reviews (WIREs). Data Mining and Knowledge Discovery 12 (2022), Nr. 3 |
|
dc.rights |
CC BY 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0 |
|
dc.subject |
benchmark datasets |
eng |
dc.subject |
bias |
eng |
dc.subject |
datasets for fairness |
eng |
dc.subject |
discrimination |
eng |
dc.subject |
fairness-aware machine learning |
eng |
dc.subject.ddc |
004 | Informatik
|
|
dc.title |
A survey on datasets for fairness-aware machine learning |
eng |
dc.type |
Article |
|
dc.type |
Text |
|
dc.relation.essn |
1942-4795 |
|
dc.relation.issn |
1942-4787 |
|
dc.relation.doi |
https://doi.org/10.1002/widm.1452 |
|
dc.bibliographicCitation.issue |
3 |
|
dc.bibliographicCitation.volume |
12 |
|
dc.bibliographicCitation.firstPage |
e1452 |
|
dc.description.version |
publishedVersion |
eng |
tib.accessRights |
frei zug�nglich |
|
dc.bibliographicCitation.articleNumber |
e1452 |
|