Paper Abstract and Keywords |
Presentation |
2017-11-09 13:00
Consequently Fair Contextual Bandit Learning Kazuto Fukuchi (Univ. of Tsukuba), Jun Sakuma (Univ. of Tsukuba/JST/RIKEN) IBISML2017-53 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Fairness in machine learning is being recognized as an important field. It requires that the consequent decisions made by a machine learning algorithm must not be biased on the sensitive attributes of individuals. In this paper, we introduce a novel notion of fairness, consequential fairness, and deal with sequential decision-making problems with a consequential fairness constraint. Consequential fairness requires almost sure satisfaction of fairness, that is, the number of unfair decisions does not exceed a prescribed threshold, $eta$, almost surely after a finite number of decisions. We first show a impossibility result for the adversarial context, which implies fundamental hardness of the setting. Meaningful results are obtained under the allocative contexts. We show that a novel algorithm, FairUcb, can achieve $O(max{ln T,min{eta,T/eta}})$ regret under a mild assumption. We also show that this regret bound is optimal up to constant if $eta le o(ln T)$. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
fairness / discrimination / contextual bandits / gap-dependent regret / adversarial contexts / allocative contexts / / |
Reference Info. |
IEICE Tech. Rep., vol. 117, no. 293, IBISML2017-53, pp. 139-146, Nov. 2017. |
Paper # |
IBISML2017-53 |
Date of Issue |
2017-11-02 (IBISML) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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IBISML2017-53 |
Conference Information |
Committee |
IBISML |
Conference Date |
2017-11-08 - 2017-11-10 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Univ. of Tokyo |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Information-Based Induction Science Workshop (IBIS2017) |
Paper Information |
Registration To |
IBISML |
Conference Code |
2017-11-IBISML |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Consequently Fair Contextual Bandit Learning |
Sub Title (in English) |
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Keyword(1) |
fairness |
Keyword(2) |
discrimination |
Keyword(3) |
contextual bandits |
Keyword(4) |
gap-dependent regret |
Keyword(5) |
adversarial contexts |
Keyword(6) |
allocative contexts |
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1st Author's Name |
Kazuto Fukuchi |
1st Author's Affiliation |
University of Tsukuba (Univ. of Tsukuba) |
2nd Author's Name |
Jun Sakuma |
2nd Author's Affiliation |
University of Tsukuba/Japan Science and Technology Agency CREST/RIKEN AIP (Univ. of Tsukuba/JST/RIKEN) |
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Speaker |
Author-1 |
Date Time |
2017-11-09 13:00:00 |
Presentation Time |
150 minutes |
Registration for |
IBISML |
Paper # |
IBISML2017-53 |
Volume (vol) |
vol.117 |
Number (no) |
no.293 |
Page |
pp.139-146 |
#Pages |
8 |
Date of Issue |
2017-11-02 (IBISML) |
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