TY - GEN
T1 - FEAT
T2 - 23rd International Conference on Discovery Science, DS 2020
AU - Zhang, Wenbin
AU - Bifet, Albert
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Fairness-aware learning is increasingly important in socially-sensitive applications for the sake of achieving optimal and non-discriminative decision-making. Most of the proposed fairness-aware learning algorithms process the data in offline settings and assume that the data is generated by a single concept without drift. Unfortunately, in many real-world applications, data is generated in a streaming fashion and can only be scanned once. In addition, the underlying generation process might also change over time. In this paper, we propose and illustrate an efficient algorithm for mining fair decision trees from discriminatory and continuously evolving data streams. This algorithm, called FEAT (Fairness-Enhancing and concept-Adapting Tree), is based on using the change detector to learn adaptively from non-stationary data streams, that also accounts for fairness. We study FEAT’s properties and demonstrate its utility through experiments on a set of discriminated and time-changing data streams.
AB - Fairness-aware learning is increasingly important in socially-sensitive applications for the sake of achieving optimal and non-discriminative decision-making. Most of the proposed fairness-aware learning algorithms process the data in offline settings and assume that the data is generated by a single concept without drift. Unfortunately, in many real-world applications, data is generated in a streaming fashion and can only be scanned once. In addition, the underlying generation process might also change over time. In this paper, we propose and illustrate an efficient algorithm for mining fair decision trees from discriminatory and continuously evolving data streams. This algorithm, called FEAT (Fairness-Enhancing and concept-Adapting Tree), is based on using the change detector to learn adaptively from non-stationary data streams, that also accounts for fairness. We study FEAT’s properties and demonstrate its utility through experiments on a set of discriminated and time-changing data streams.
KW - AI ethics
KW - Online classification
KW - Online fairness
U2 - 10.1007/978-3-030-61527-7_12
DO - 10.1007/978-3-030-61527-7_12
M3 - Conference contribution
AN - SCOPUS:85094149575
SN - 9783030615260
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 175
EP - 189
BT - Discovery Science - 23rd International Conference, DS 2020, Proceedings
A2 - Appice, Annalisa
A2 - Tsoumakas, Grigorios
A2 - Manolopoulos, Yannis
A2 - Matwin, Stan
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 19 October 2020 through 21 October 2020
ER -