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
Zusammenfassung: | |
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. | |
Lizenzbestimmungen: | CC BY 4.0 Unported |
Publikationstyp: | Article |
Publikationsstatus: | publishedVersion |
Erstveröffentlichung: | 2022 |
Die Publikation erscheint in Sammlung(en): | Forschungszentren |
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