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Paper Abstract and Keywords
Presentation 2024-03-04 10:40
Poisoning Attack on Fairness of Fair Classification Algorithm through Threshold Control
Dai Shengtian, Akimoto Youhei (Univ. of Tsukuba/RIKEN), Jun Sakuma (Tokyo Tech./RIKEN), Fukuchi Kazuto (Univ. of Tsukuba/RIKEN) IBISML2023-47
Abstract (in Japanese) (See Japanese page) 
(in English) The ethical issues of artificial intelligence have become more severe as machine learning is widely used in several fields. Recent developments in machine learning enable machine learning algorithms to mitigate the ethical issue of fairness, a problem of discriminating output against sensitive attributes, including gender and race. However, a poisoning attack, originally designed to harm the accuracy of models, can also introduce unfair bias into models. Our research investigated the attack of worsening fairness of the fair learning models, finding how the fair learning models behave under the fairness attack. Specifically, we construct an attack strategy, TAF, targeting the fairness of the fair learning model by controlling the thresholds involved in the model and elucidate its behavior. The experimental results demonstrate that TAF does more harm to the fairness of the fair learning model than the attack methods proposed in existing studies
Keyword (in Japanese) (See Japanese page) 
(in English) fair learning / poisoning attack / / / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 410, IBISML2023-47, pp. 49-56, March 2024.
Paper # IBISML2023-47 
Date of Issue 2024-02-25 (IBISML) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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)
Download PDF IBISML2023-47

Conference Information
Committee PRMU IBISML IPSJ-CVIM  
Conference Date 2024-03-03 - 2024-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Hiroshima Univ. Higashi-Hiroshima campus 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IBISML 
Conference Code 2024-03-PRMU-IBISML-CVIM 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Poisoning Attack on Fairness of Fair Classification Algorithm through Threshold Control 
Sub Title (in English)  
Keyword(1) fair learning  
Keyword(2) poisoning attack  
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1st Author's Name Dai Shengtian  
1st Author's Affiliation University of Tsukuba/RIKEN (Univ. of Tsukuba/RIKEN)
2nd Author's Name Akimoto Youhei  
2nd Author's Affiliation University of Tsukuba/RIKEN (Univ. of Tsukuba/RIKEN)
3rd Author's Name Jun Sakuma  
3rd Author's Affiliation Tokyo Institute of Technology/RIKEN (Tokyo Tech./RIKEN)
4th Author's Name Fukuchi Kazuto  
4th Author's Affiliation University of Tsukuba/RIKEN (Univ. of Tsukuba/RIKEN)
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Speaker Author-1 
Date Time 2024-03-04 10:40:00 
Presentation Time 15 minutes 
Registration for IBISML 
Paper # IBISML2023-47 
Volume (vol) vol.123 
Number (no) no.410 
Page pp.49-56 
#Pages 8 
Date of Issue 2024-02-25 (IBISML) 


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