Fairness Testing

Target Conference:

IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW).

Relevant Papers:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. John, Philips George, Deepak Vijaykeerthy, and Diptikalyan Saha. "Verifying individual fairness in machine learning models." Conference on Uncertainty in Artificial Intelligence. PMLR, 2020.
  6. 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.
  7. 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.
  8. Zhang, Peixin, et al. "White-box fairness testing through adversarial sampling." Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering. 2020.
  9. 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.
  10. 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.
  11. 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.
  12. Vassøy, Bjørnar, Helge Langseth, and Benjamin Kille. "Providing previously unseen users fair recommendations using variational autoencoders." Proceedings of the 17th ACM Conference on Recommender Systems. 2023.
  13. Louizos, Christos, et al. "The variational fair autoencoder." arXiv preprint arXiv:1511.00830 (2015).
  14. Panagiotou, Emmanouil, Arjun Roy, and Eirini Ntoutsi. "Synthetic tabular data generation for class imbalance and fairness: A comparative study." arXiv preprint arXiv:2409.05215 (2024).