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research-article

NeuronFair: interpretable white-box fairness testing through biased neuron identification

Authors:

Jinyin ChenAuthors Info & Claims

ICSE '22: Proceedings of the 44th International Conference on Software Engineering

Pages 1519 - 1531

https://doi.org/10.1145/3510003.3510123

Published: 05 July 2022 Publication History

Abstract

Deep neural networks (DNNs) have demonstrated their outperformance in various domains. However, it raises a social concern whether DNNs can produce reliable and fair decisions especially when they are applied to sensitive domains involving valuable resource allocation, such as education, loan, and employment. It is crucial to conduct fairness testing before DNNs are reliably deployed to such sensitive domains, i.e., generating as many instances as possible to uncover fairness violations. However, the existing testing methods are still limited from three aspects: interpretability, performance, and generalizability. To overcome the challenges, we propose NeuronFair, a new DNN fairness testing framework that differs from previous work in several key aspects: (1) interpretable - it quantitatively interprets DNNs' fairness violations for the biased decision; (2) effective - it uses the interpretation results to guide the generation of more diverse instances in less time; (3) generic - it can handle both structured and unstructured data. Extensive evaluations across 7 datasets and the corresponding DNNs demonstrate NeuronFair's superior performance. For instance, on structured datasets, it generates much more instances (~ ×5.84) and saves more time (with an average speedup of 534.56%) compared with the state-of-the-art methods. Besides, the instances of NeuronFair can also be leveraged to improve the fairness of the biased DNNs, which helps build more fair and trustworthy deep learning systems. The code of NeuronFair is open-sourced at https://github.com/haibinzheng/NeuronFair.

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Cited By

Chen JZhang YZhang LZhang MWan CSu TPu GFilkov VRay BZhou M(2024)FIPSER: Improving Fairness Testing of DNN by Seed PrioritizationProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695486(1069-1081)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695486
Zhao ZToda TKitamura TFilkov VRay BZhou M(2024)Approximation-guided Fairness Testing through Discriminatory Space AnalysisProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695481(1007-1018)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695481
Du CChen T(2024)Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning SystemsProceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3674805.3686673(107-118)Online publication date: 24-Oct-2024
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Show More Cited By

Index Terms

NeuronFair: interpretable white-box fairness testing through biased neuron identification
1. Computing methodologies
  1. Artificial intelligence
2. Software and its engineering
  1. Software organization and properties
    1. Extra-functional properties
      1. Software reliability

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cover image ACM Conferences

ICSE '22: Proceedings of the 44th International Conference on Software Engineering

May 2022

2508 pages

ISBN:9781450392211

DOI:10.1145/3510003

General Chair:
Matthew B Dwyer
University of Virginia
,
Program Chairs:
Daniela Damian
University of Victoria, Canada
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Andreas Zeller
CISPA, Germany

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ICSE '22: 44th International Conference on Software Engineering

May 21 - 29, 2022

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Cited By

Chen JZhang YZhang LZhang MWan CSu TPu GFilkov VRay BZhou M(2024)FIPSER: Improving Fairness Testing of DNN by Seed PrioritizationProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695486(1069-1081)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695486
Zhao ZToda TKitamura TFilkov VRay BZhou M(2024)Approximation-guided Fairness Testing through Discriminatory Space AnalysisProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695481(1007-1018)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695481
Du CChen T(2024)Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning SystemsProceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3674805.3686673(107-118)Online publication date: 24-Oct-2024
https://dl.acm.org/doi/10.1145/3674805.3686673
Sun ZChen ZZhang JHao D(2024)Fairness Testing of Machine Translation SystemsACM Transactions on Software Engineering and Methodology10.1145/366460833:6(1-27)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3664608
Chen ZZhang JHort MHarman MSarro F(2024)Fairness Testing: A Comprehensive Survey and Analysis of TrendsACM Transactions on Software Engineering and Methodology10.1145/365215533:5(1-59)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3652155
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