dc.contributor.author | Zhang, P | |
dc.contributor.author | Wang, J | |
dc.contributor.author | Sun, J | |
dc.contributor.author | Dong, G | |
dc.contributor.author | Wang, X | |
dc.contributor.author | Wang, X | |
dc.contributor.author | Dong, JS | |
dc.contributor.author | Dai, T | |
dc.date.accessioned | 2020-11-18T03:06:40Z | |
dc.date.available | 2020-11-18T03:06:40Z | |
dc.date.issued | 2020 | |
dc.identifier.isbn | 9781450371216 | |
dc.identifier.issn | 0270-5257 | |
dc.identifier.doi | 10.1145/3377811.3380331 | |
dc.identifier.uri | http://hdl.handle.net/10072/399420 | |
dc.description.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). | |
dc.description.peerreviewed | Yes | |
dc.publisher | Association for Computing Machinery (ACM) | |
dc.relation.ispartofconferencename | ACM/IEEE 42nd International Conference on Software Engineering (ICSE 2020) | |
dc.relation.ispartofconferencetitle | Proceedings 2020 ACM/IEEE 42nd International Conference on Software Engineering, ICSE 2020 | |
dc.relation.ispartofdatefrom | 2020-06-27 | |
dc.relation.ispartofdateto | 2020-07-19 | |
dc.relation.ispartoflocation | Seoul, South Korea | |
dc.relation.ispartofpagefrom | 949 | |
dc.relation.ispartofpageto | 960 | |
dc.subject.fieldofresearch | Artificial intelligence | |
dc.subject.fieldofresearchcode | 4602 | |
dc.title | White-box fairness testing through adversarial sampling | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dcterms.bibliographicCitation | Zhang, P; Wang, J; Sun, J; Dong, G; Wang, X; Wang, X; Dong, JS; Dai, T, White-box fairness testing through adversarial sampling, Proceedings 2020 ACM/IEEE 42nd International Conference on Software Engineering, ICSE 2020, 2020, pp. 949-960 | |
dc.date.updated | 2020-11-18T03:03:55Z | |
dc.description.version | Accepted Manuscript (AM) | |
gro.rights.copyright | © 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 | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Dong, Jin-Song | |