Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Bias in Information, Algorithms, and Systems |
| Untertitel | Proceedings of the International Workshop on Bias in Information, Algorithms, and Systems co-located with 13th International Conference on Transforming Digital Worlds (iConference 2018) |
| Seiten | 24-29 |
| Seitenumfang | 6 |
| Publikationsstatus | Veröffentlicht - 2018 |
| Veranstaltung | 2018 International Workshop on Bias in Information, Algorithms, and Systems, BIAS 2018 - Sheffield, Großbritannien / Vereinigtes Königreich Dauer: 25 März 2018 → 25 März 2018 |
Publikationsreihe
| Name | CEUR Workshop Proceedings |
|---|---|
| Herausgeber (Verlag) | CEUR Workshop Proceedings |
| Band | 2103 |
| ISSN (Print) | 1613-0073 |
Abstract
There is an increasing amount of work from different communities in data mining, machine learning, information retrieval, semantic web, and databases on bias discovery and discrimination-aware learning with the goal of developing not only good quality models but also models that account for fairness. In this work, we focus on supervised learning where biases towards certain attributes like race or gender might exist.We propose data augmentation techniques to correct for bias at the input/data layer. Our experiments with real world datasets show the potential of augmentation techniques for dealing with bias.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Bias in Information, Algorithms, and Systems: Proceedings of the International Workshop on Bias in Information, Algorithms, and Systems co-located with 13th International Conference on Transforming Digital Worlds (iConference 2018). 2018. S. 24-29 (CEUR Workshop Proceedings; Band 2103).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Dealing with Bias via Data Augmentation in Supervised Learning Scenarios
AU - Iosifidis, Vasileios
AU - Ntoutsi, Eirini
N1 - Funding information: The work was partially funded by the European Commission for the ERC Advanced Grant ALEXANDRIA under grant No. 339233 and by the German Research Foundation (DFG) project OSCAR (Opinion Stream Classification with Ensembles and Active leaRners) No. 317686254.
PY - 2018
Y1 - 2018
N2 - There is an increasing amount of work from different communities in data mining, machine learning, information retrieval, semantic web, and databases on bias discovery and discrimination-aware learning with the goal of developing not only good quality models but also models that account for fairness. In this work, we focus on supervised learning where biases towards certain attributes like race or gender might exist.We propose data augmentation techniques to correct for bias at the input/data layer. Our experiments with real world datasets show the potential of augmentation techniques for dealing with bias.
AB - There is an increasing amount of work from different communities in data mining, machine learning, information retrieval, semantic web, and databases on bias discovery and discrimination-aware learning with the goal of developing not only good quality models but also models that account for fairness. In this work, we focus on supervised learning where biases towards certain attributes like race or gender might exist.We propose data augmentation techniques to correct for bias at the input/data layer. Our experiments with real world datasets show the potential of augmentation techniques for dealing with bias.
UR - http://www.scopus.com/inward/record.url?scp=85048346636&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85048346636
T3 - CEUR Workshop Proceedings
SP - 24
EP - 29
BT - Bias in Information, Algorithms, and Systems
T2 - 2018 International Workshop on Bias in Information, Algorithms, and Systems, BIAS 2018
Y2 - 25 March 2018 through 25 March 2018
ER -