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Machine Learning (ML) software has been widely adopted in modern society, with reported fairness implications for minority groups based on race, sex, age, etc.
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2013
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2014
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2015
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2015
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2015
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2019
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2016
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M. Hardt, E. Price, E. Price, and N. Srebro, “Equality of opportunity in supervised learning,” in Advances in Neural Information Processing Systems , D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, Eds., vol. 29. Curran Associates, Inc., 2016. [Online]. Available: https://proceedings.neurips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf
2016
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2016
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A. Chouldechova, “Fair prediction with disparate impact: A study of bias in recidivism prediction instruments,” Big data , vol. 5 2, pp. 153–163, 2017
2017
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2017
Cited alongside, same era.
2017
Cited alongside, same era.
N. Grgic-Hlaca, M. Zafar, K. P. Gummadi, and A. Weller, “On fairness, diversity and randomness in algorithmic decision making,” 06 2017
2017
Cited alongside, same era.
2017
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G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, and K. Q. Weinberger, “On fairness and calibration,” in Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., vol. 30. Curran Associates, Inc., 2017. [Online]. Available: https://proceedings.neurips.cc/paper/2017/file/b8b9c74ac526fffbeb2d39ab038d1cd7-Paper.pdf
2017
Cited alongside, same era.
D. Bhaskaruni, H. Hu, and C. Lan, “Improving prediction fairness via model ensemble,” in 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) , 2019, pp. 1810–1814
2019
Later among the works it cites.
2019
Later among the works it cites.
K. Holstein, J. Wortman Vaughan, H. Daumé, M. Dudik, and H. Wallach, “Improving fairness in machine learning systems: What do industry practitioners need?” in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems , ser. CHI ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 1–16
2019
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S. Biswas and H. Rajan, “Do the machine learning models on a crowd sourced platform exhibit bias? an empirical study on model fairness,” ser. ESEC/FSE 2020. New York, NY, USA: Association for Computing Machinery, 2020, p. 642–653. [Online]. Available: https://doi.org/10.1145/3368089.3409704
2020
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J. Chakraborty, S. Majumder, Z. Yu, and T. Menzies, Fairway: A Way to Build Fair ML Software . New York, NY, USA: Association for Computing Machinery, 2020, p. 654–665. [Online]. Available: https://doi.org/10.1145/3368089.3409697
2020
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S. Biswas and H. Rajan, “Fair preprocessing: Towards understanding compositional fairness of data transformers in machine learning pipeline,” in Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , ser. ESEC/FSE 2021. New York, NY, USA: Association for Computing Machinery, 2021, p. 981–993. [Online]. Available: https://doi.org/10.1145/3468264.3468536
2021
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Y. Bian and H. Chen, “When does diversity help generalization in classification ensembles?” IEEE Transactions on Cybernetics , pp. 1–17, 2021
2021
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P. J. Kenfack, A. M. Khan, S. A. Kazmi, R. Hussain, A. Oracevic, and A. M. Khattak, “Impact of model ensemble on the fairness of classifiers in machine learning,” in 2021 International Conference on Applied Artificial Intelligence (ICAPAI) , 2021, pp. 1–6
2021
Later among the works it cites.
J. Chakraborty, S. Majumder, and T. Menzies, “Bias in machine learning software: Why? how? what to do?” in Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , ser. ESEC/FSE 2021. New York, NY, USA: Association for Computing Machinery, 2021, p. 429–440. [Online]. Available: https://doi.org/10.1145/3468264.3468537
2021
Later among the works it cites.
J. M. Zhang and M. Harman, “”ignorance and prejudice” in software fairness,” in 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) , 2021, pp. 1436–1447
2021
Later among the works it cites.
2021
Later among the works it cites.
2022
Closest in time.
S. Tizpaz-Niari, A. Kumar, G. Tan, and A. Trivedi, “Fairness-aware configuration of machine learning libraries,” ser. ICSE ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 909–920. [Online]. Available: https://doi.org/10.1145/3510003.3510202
2022
Closest in time.
Z. Chen, J. M. Zhang, F. Sarro, and M. Harman, “Maat: A novel ensemble approach to addressing fairness and performance bugs for machine learning software,” in Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , ser. ESEC/FSE 2022. New York, NY, USA: Association for Computing Machinery, 2022, p. 1122–1134. [Online]. Available: https://doi.org/10.1145/3540250.3549093
2022
Closest in time.
H. Zheng, Z. Chen, T. Du, X. Zhang, Y. Cheng, S. Ti, J. Wang, Y. Yu, and J. Chen, “Neuronfair: Interpretable white-box fairness testing through biased neuron identification,” in 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) , 2022, pp. 1519–1531
2022
Closest in time.
Y. Li, L. Meng, L. Chen, L. Yu, D. Wu, Y. Zhou, and B. Xu, “Training data debugging for the fairness of machine learning software,” in 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) , 2022, pp. 2215–2227
2022
Closest in time.
S. Biswas and H. Rajan, “Fairify: Fairness verification of neural networks,” in ICSE’2023: The 45th International Conference on Software Engineering , May 14-May 20 2023
2023
Closest in time.
S. Biswas, M. Wardat, and H. Rajan, “The art and practice of data science pipelines: A comprehensive study of data science pipelines in theory, in-the-small, and in-the-large,” in Proceedings of the 44th International Conference on Software Engineering , ser. ICSE ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 2091–2103. [Online]. Available: https://doi.org/10.1145/3510003.3510057
2091
Closest in time.