Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

10-2020

Abstract

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) .

Discipline

Information Security | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, Seoul, October 5-11

First Page

949

Last Page

960

ISBN

9781450371216

Identifier

10.1145/3377811.3380331

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3377811.3380331

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