Fairness Testing
Target Conference:
International Workshop on Combinatorial Testing (IWCT 2025)
Summary:
Decision-making by Machine Learning (ML) models can exhibit biased behavior, resulting in unfair outcomes.
Testing ML models for such biases is essential to ensure unbiased decision-making.
In this work, we propose a combinatorial testing-based approach in the latent space of a generative model to generate instances
that assess the fairness of black-box ML models.
Our approach involves a two-step process: generating t-way test cases in the latent space of a Variational AutoEncoder and
performing fairness testing using the instances reconstructed from these test cases.
Relevant Papers:
- Chen, Zhenpeng, et al. "Fairness testing: A comprehensive survey and analysis of trends." ACM Transactions on Software Engineering and Methodology 33.5 (2024): 1-59.
- Xiao, Yisong, et al. "Latent imitator: Generating natural individual discriminatory instances for black-box fairness testing." Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis. 2023.
- Aggarwal, Aniya, et al. "Black box fairness testing of machine learning models." Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 2019.
- Wang, Zhaohui, et al. "MAFT: Efficient Model-Agnostic Fairness Testing for Deep Neural Networks via Zero-Order Gradient Search." Proceedings of the IEEE/ACM 46th International Conference on Software Engineering. 2024.
- Khadka, Krishna, et al. "Synthetic data generation using combinatorial testing and variational autoencoder." 2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2023.
- Patel, Ankita Ramjibhai, et al. "A combinatorial approach to fairness testing of machine learning models." 2022 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2022.
- Kacker, Raghu N., et al. "Factorials experiments, covering arrays, and combinatorial testing." Mathematics in Computer Science 15 (2021): 715-739.
- Lei, Yu, et al. "IPOG: A general strategy for t-way software testing." 14th Annual IEEE International Conference and Workshops on the Engineering of Computer-Based Systems (ECBS'07). IEEE, 2007.
- Bellamy, Rachel KE, et al. "AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias." IBM Journal of Research and Development 63.4/5 (2019): 4-1.
- Mothilal, Ramaravind K., Amit Sharma, and Chenhao Tan. "Explaining machine learning classifiers through diverse counterfactual explanations." Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 2020.
- Udeshi, Sakshi, Pryanshu Arora, and Sudipta Chattopadhyay. "Automated directed fairness testing." Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering. 2018.
- Kingma, Diederik P., and Max Welling. "An introduction to variational autoencoders." Foundations and Trends® in Machine Learning 12.4 (2019): 307-392.
- Fayyad, Usama M., and Keki B. Irani. "Multi-interval discretization of continuous-valued attributes for classification learning." IJCAI. Vol. 93. No. 2. 1993.
- Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "Model-agnostic interpretability of machine learning." arXiv preprint arXiv:1606.05386 (2016).
- Jiang, Weipeng, et al. "Black-Box Fairness Testing with Shadow Models." International Conference on Information and Communications Security. Singapore: Springer Nature Singapore, 2023.
- Fan, Ming, et al. "Explanation-guided fairness testing through genetic algorithm." Proceedings of the 44th International Conference on Software Engineering. 2022.
- Zheng, Haibin, et al. "Neuronfair: Interpretable white-box fairness testing through biased neuron identification." Proceedings of the 44th International Conference on Software Engineering. 2022.
- Galhotra, Sainyam, Yuriy Brun, and Alexandra Meliou. "Fairness testing: Testing software for discrimination." Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering. 2017.
- Perez Morales, Daniel, Takashi Kitamura, and Shingo Takada. "Coverage-guided fairness testing." International Conference on Intelligence Science. Cham: Springer International Publishing, 2021.
- Kitamura, Takashi, Zhenjiang Zhao, and Takahisa Toda. "Applying combinatorial testing to verification-based fairness testing." International Symposium on Search Based Software Engineering. Cham: Springer International Publishing, 2022.
- Yin, Ziqiang, Wentian Zhao, and Tian Song. "Boundary-Guided Black-Box Fairness Testing." 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2024.
- Tao, Guanhong, et al. "RULER: Discriminative and iterative adversarial training for deep neural network fairness." Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 2022.
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- Mickisch, David, et al. "Understanding the decision boundary of deep neural networks: An empirical study." arXiv preprint arXiv:2002.01810 (2020).
- Flores, Anthony W., Kristin Bechtel, and Christopher T. Lowenkamp. "False positives, false negatives, and false analyses: A rejoinder to machine bias: There's software used across the country to predict future criminals. And it's biased against blacks." Federal Probation 80 (2016): 38.
- John, Philips George, Deepak Vijaykeerthy, and Diptikalyan Saha. "Verifying individual fairness in machine learning models." Conference on Uncertainty in Artificial Intelligence. PMLR, 2020.
- Cabrera, Ángel Alexander, et al. "FairVis: Visual analytics for discovering intersectional bias in machine learning." 2019 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, 2019.
- Monjezi, Verya, et al. "Information-theoretic testing and debugging of fairness defects in deep neural networks." 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE). IEEE, 2023.
- Zhang, Peixin, et al. "White-box fairness testing through adversarial sampling." Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering. 2020.
- Liu, Zeyuan, Xin Zhang, and Benben Jiang. "Active learning with fairness-aware clustering for fair classification considering multiple sensitive attributes." Information Sciences 647 (2023): 119521.
- Haffar, Rami, et al. "Measuring fairness in machine learning models via counterfactual examples." International Conference on Modeling Decisions for Artificial Intelligence. Cham: Springer International Publishing, 2022.
- Dash, Saloni, Vineeth N. Balasubramanian, and Amit Sharma. "Evaluating and mitigating bias in image classifiers: A causal perspective using counterfactuals." Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2022.
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