White-box Fairness Testing through Adversarial Sampling
Technical
Although deep neural networks (DNNs) have demonstrated astonishing performance in many applications, there are still concerns on their dependability. One desirable property of DNN for applications with societal impact is fairness (i.e., non-discrimination). In this work, we propose a scalable approach for searching individual discriminatory instances of DNN. Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which makes it significantly more scalable than existing methods. Experimental results show that our approach explores the search space more effectively (9 times) and generates much more individual discriminatory instances (25 times) using much less time (half to 1/7).
Tue 7 JulDisplayed time zone: (UTC) Coordinated Universal Time change
08:05 - 09:05 | I5-Deep Learning Testing and DebuggingTechnical Papers / Demonstrations at Goguryeo Chair(s): Pooyan Jamshidi University of South Carolina | ||
08:05 12mTalk | DISSECTOR: Input Validation for Deep Learning Applications by Crossing-layer DissectionTechnical Technical Papers Huiyan Wang State Key Lab. for Novel Software Tech. and Dept. of Comp. Sci. and Tech., Nanjing University, Nanjing, China, Jingwei Xu Nanjing University, Chang Xu Nanjing University, Xiaoxing Ma Nanjing University, Jian Lu Nanjing University | ||
08:17 12mTalk | White-box Fairness Testing through Adversarial Sampling Technical Papers Peixin Zhang Zhejiang University, Jingyi Wang National University of Singapore, Singapore, Jun Sun Singapore Management University, Guoliang Dong Computer College of Zhejiang University, Xinyu Wang Zhejiang University, Xingen Wang Zhejiang University, Jin Song Dong National University of Singapore, Dai Ting Huawei Corporation | ||
08:29 3mTalk | FeatureNET: Diversity-driven Generation of Deep Learning ModelsDemo Demonstrations Salah Ghamizi SntT - University of Luxembourg, Maxime Cordy SnT, University of Luxembourg, Mike Papadakis University of Luxembourg, Yves Le Traon University of Luxembourg | ||
08:32 3mTalk | EvalDNN: A Toolbox for Evaluating Deep Neural Network ModelsDemo Demonstrations Yongqiang TIAN The Hong Kong University of Science and Technology, Zhihua Zeng Zhejiang University, Ming Wen Huazhong University of Science and Technology, China, Yepang Liu Southern University of Science and Technology, Tzu-yang Kuo The Hong Kong University of Science and Technology, Shing-Chi Cheung Department of Computer Science and Engineering, The Hong Kong University of Science and Technology | ||
08:35 12mTalk | Taxonomy of Real Faults in Deep Learning Systems Technical Papers Nargiz Humbatova Università della Svizzera italiana, Gunel Jahangirova Università della Svizzera italiana, Gabriele Bavota Università della Svizzera italiana, Vincenzo Riccio Università della Svizzera italiana, Andrea Stocco Università della Svizzera italiana, Paolo Tonella Università della Svizzera italiana | ||
08:47 12mTalk | An Empirical Study on Program Failures of Deep Learning Jobs Technical Papers Ru Zhang Microsoft Research, Wencong Xiao Alibaba, Hongyu Zhang University of Newcastle, Australia, Yu Liu Microsoft Research, Haoxiang Lin Microsoft Research, Mao Yang Microsoft Research DOI Pre-print | ||