Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Advances in Knowledge Discovery and Data Mining |
| Untertitel | 25th Pacific-Asia Conference, PAKDD 2021, Proceedings |
| Herausgeber/-innen | Kamal Karlapalem, Hong Cheng, Naren Ramakrishnan, R. K. Agrawal, P. Krishna Reddy, Jaideep Srivastava, Tanmoy Chakraborty |
| Erscheinungsort | Cham |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 245-256 |
| Seitenumfang | 12 |
| ISBN (elektronisch) | 978-3-030-75765-6 |
| ISBN (Print) | 9783030757649 |
| Publikationsstatus | Veröffentlicht - 8 Mai 2021 |
| Veranstaltung | 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021 - Virtual, Online Dauer: 11 Mai 2021 → 14 Mai 2021 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 12713 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
As Artificial Intelligence (AI) is used in more applications, the need to consider and mitigate biases from the learned models has followed. Most works in developing fair learning algorithms focus on the offline setting. However, in many real-world applications data comes in an online fashion and needs to be processed on the fly. Moreover, in practical application, there is a trade-off between accuracy and fairness that needs to be accounted for, but current methods often have multiple hyper-parameters with non-trivial interaction to achieve fairness. In this paper, we propose a flexible ensemble algorithm for fair decision-making in the more challenging context of evolving online settings. This algorithm, called FARF (Fair and Adaptive Random Forests), is based on using online component classifiers and updating them according to the current distribution, that also accounts for fairness and a single hyper-parameters that alters fairness-accuracy balance. Experiments on real-world discriminated data streams demonstrate the utility of FARF.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Advances in Knowledge Discovery and Data Mining: 25th Pacific-Asia Conference, PAKDD 2021, Proceedings. Hrsg. / Kamal Karlapalem; Hong Cheng; Naren Ramakrishnan; R. K. Agrawal; P. Krishna Reddy; Jaideep Srivastava; Tanmoy Chakraborty. Cham: Springer Science and Business Media Deutschland GmbH, 2021. S. 245-256 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12713 LNAI).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - FARF
T2 - 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021
AU - Zhang, Wenbin
AU - Bifet, Albert
AU - Zhang, Xiangliang
AU - Weiss, Jeremy C.
AU - Nejdl, Wolfgang
PY - 2021/5/8
Y1 - 2021/5/8
N2 - As Artificial Intelligence (AI) is used in more applications, the need to consider and mitigate biases from the learned models has followed. Most works in developing fair learning algorithms focus on the offline setting. However, in many real-world applications data comes in an online fashion and needs to be processed on the fly. Moreover, in practical application, there is a trade-off between accuracy and fairness that needs to be accounted for, but current methods often have multiple hyper-parameters with non-trivial interaction to achieve fairness. In this paper, we propose a flexible ensemble algorithm for fair decision-making in the more challenging context of evolving online settings. This algorithm, called FARF (Fair and Adaptive Random Forests), is based on using online component classifiers and updating them according to the current distribution, that also accounts for fairness and a single hyper-parameters that alters fairness-accuracy balance. Experiments on real-world discriminated data streams demonstrate the utility of FARF.
AB - As Artificial Intelligence (AI) is used in more applications, the need to consider and mitigate biases from the learned models has followed. Most works in developing fair learning algorithms focus on the offline setting. However, in many real-world applications data comes in an online fashion and needs to be processed on the fly. Moreover, in practical application, there is a trade-off between accuracy and fairness that needs to be accounted for, but current methods often have multiple hyper-parameters with non-trivial interaction to achieve fairness. In this paper, we propose a flexible ensemble algorithm for fair decision-making in the more challenging context of evolving online settings. This algorithm, called FARF (Fair and Adaptive Random Forests), is based on using online component classifiers and updating them according to the current distribution, that also accounts for fairness and a single hyper-parameters that alters fairness-accuracy balance. Experiments on real-world discriminated data streams demonstrate the utility of FARF.
UR - http://www.scopus.com/inward/record.url?scp=85111099364&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-75765-6_20
DO - 10.1007/978-3-030-75765-6_20
M3 - Conference contribution
AN - SCOPUS:85111099364
SN - 9783030757649
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 245
EP - 256
BT - Advances in Knowledge Discovery and Data Mining
A2 - Karlapalem, Kamal
A2 - Cheng, Hong
A2 - Ramakrishnan, Naren
A2 - Agrawal, R. K.
A2 - Reddy, P. Krishna
A2 - Srivastava, Jaideep
A2 - Chakraborty, Tanmoy
PB - Springer Science and Business Media Deutschland GmbH
CY - Cham
Y2 - 11 May 2021 through 14 May 2021
ER -