Automatic Fairness Testing of Machine Learning Models
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-64881-7_16
Reference24 articles.
1. Aggarwal, A., Lohia, P., Nagar, S., Dey, K., Saha, D.: Black box fairness testing of machine learning models. In: Proceedings of the ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/SIGSOFT FSE, pp. 625–635 (2019). https://doi.org/10.1145/3338906.3338937
2. Briand, L.C., Labiche, Y., Bawar, Z., Spido, N.T.: Using machine learning to refine category-partition test specifications and test suites. Inf. Softw. Technol. 51(11), 1551–1564 (2009). https://doi.org/10.1016/j.infsof.2009.06.006
3. Calders, T., Kamiran, F., Pechenizkiy, M.: Building classifiers with independency constraints. In: ICDM Workshops 2009, IEEE International Conference on Data Mining Workshops, Miami, Florida, USA, 6 December 2009, pp. 13–18 (2009). https://doi.org/10.1109/ICDMW.2009.83
4. Carlini, N., Wagner, D.A.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy, SP, pp. 39–57 (2017). https://doi.org/10.1109/SP.2017.49
5. Chouldechova, A.: Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5(2), 153–163 (2017). https://doi.org/10.1089/big.2016.0047
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