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  • White-box fairness testing through adversarial sampling

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    Dong449243-Accepted.pdf (1022.Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Zhang, P
    Wang, J
    Sun, J
    Dong, G
    Wang, X
    Wang, X
    Dong, JS
    Dai, T
    Griffith University Author(s)
    Dong, Jin-Song
    Year published
    2020
    Metadata
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    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 ...
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    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).
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    Conference Title
    Proceedings 2020 ACM/IEEE 42nd International Conference on Software Engineering, ICSE 2020
    DOI
    https://doi.org/10.1145/3377811.3380331
    Copyright Statement
    © ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, ISBN: 978-1-4503-7121-6, https://doi.org/10.1145/3377811.3380331
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/399420
    Collection
    • Conference outputs

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