| Reference: | Aggarwal, A., Lohia, P., Nagar, S., Dey, K., and Saha, D. (2019). Black box fairness
testing of machine learning models. In Proceedings of the 2019 27th ACM Joint Meeting
on European Software Engineering Conference and Symposium on the Foundations of
Software Engineering, pages 625–635.
Albarghouthi, A., D’Antoni, L., Drews, S., and Nori, A. V. (2017). Fairsquare: prob-
abilistic verification of program fairness. Proceedings of the ACM on Programming
Languages, 1(OOPSLA):1–30.
Awwad, Y., Fletcher, R., Frey, D., Gandhi, A., Najafian, M., and Teodorescu, M. (2020).
Exploring fairness in machine learning for international development. Technical report,
CITE MIT D-Lab.
Bastani, O., Zhang, X., and Solar-Lezama, A. (2019). Probabilistic verification of fairness
properties via concentration. Proceedings of the ACM on Programming Languages,
3(OOPSLA):1–27.
Biswas, S. and Rajan, H. (2020). Do the machine learning models on a crowd sourced
platform exhibit bias? an empirical study on model fairness. In Proceedings of the 28th
ACM joint meeting on European software engineering conference and symposium on
the foundations of software engineering, pages 642–653.
Biswas, S. and Rajan, H. (2021). 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, pages 981–993.
Biswas, S. and Rajan, H. (2023). Fairify: Fairness verification of neural networks. In
2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE), pages
1546–1558. IEEE.
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel,
L. D., Monfort, M., Muller, U., Zhang, J., et al. (2016). End to end learning for self-
driving cars. arXiv preprint arXiv:1604.07316.
Buolamwini, J. and Gebru, T. (2018). Gender shades: Intersectional accuracy disparities
in commercial gender classification. In Conference on fairness, accountability and
transparency, pages 77–91. PMLR.
Chakraborty, J., Majumder, S., Yu, Z., and Menzies, T. (2020). Fairway: a way to build
fair ml software. In Proceedings of the 28th ACM Joint Meeting on European Software
Engineering Conference and Symposium on the Foundations of Software Engineering,
pages 654–665.
Chen, Y.-F., Tsai, W.-L., Wu, W.-C., Yen, D.-D., and Yu, F. (2021). Pyct: A python con-
colic tester. In Programming Languages and Systems: 19th Asian Symposium, APLAS
2021, Chicago, IL, USA, October 17–18, 2021, Proceedings 19, pages 38–46. Springer.
Chen, Z., Zhang, J. M., Sarro, F., and Harman, M. (2024). Fairness improvement with
multiple protected attributes: How far are we? In Proceedings of the IEEE/ACM 46th
International Conference on Software Engineering, pages 1–13.
Dastin, J. (2022). Amazon scraps secret ai recruiting tool that showed bias against women.
In Ethics of data and analytics, pages 296–299. Auerbach Publications.
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012). Fairness through
awareness. In Proceedings of the 3rd innovations in theoretical computer science con-
ference, pages 214–226.
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., and Venkatasubramanian, S.
(2015). Certifying and removing disparate impact. In proceedings of the 21th ACM
SIGKDD international conference on knowledge discovery and data mining, pages
259–268.
Flores, A. W., Bechtel, K., and Lowenkamp, C. T. (2016). False positives, false nega-
tives, 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. Fed. Probation,
80:38.
Galhotra, S., Brun, Y., and Meliou, A. (2017). Fairness testing: testing software for dis-
crimination. In Proceedings of the 2017 11th Joint meeting on foundations of software
engineering, pages 498–510.
Gehr, T., Mirman, M., Drachsler-Cohen, D., Tsankov, P., Chaudhuri, S., and Vechev, M.
(2018). Ai2: Safety and robustness certification of neural networks with abstract inter-
pretation. In 2018 IEEE symposium on security and privacy (SP), pages 3–18. IEEE.
Gohar, U., Biswas, S., and Rajan, H. (2023). Towards understanding fairness and its
composition in ensemble machine learning. In 2023 IEEE/ACM 45th International
Conference on Software Engineering (ICSE), pages 1533–1545. IEEE.
Goodfellow, I. J., Shlens, J., and Szegedy, C. (2014). Explaining and harnessing adver-
sarial examples. arXiv preprint arXiv:1412.6572.
Hort, M., Zhang, J. M., Sarro, F., and Harman, M. (2021). Fairea: A model behaviour
mutation approach to benchmarking bias mitigation methods. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on
the Foundations of Software Engineering, pages 994–1006.
John, P. G., Vijaykeerthy, D., and Saha, D. (2020). Verifying individual fairness in machine
learning models. In Conference on Uncertainty in Artificial Intelligence, pages 749–
758. PMLR.
Katz, G., Barrett, C., Dill, D. L., Julian, K., and Kochenderfer, M. J. (2017). Reluplex: An
efficient smt solver for verifying deep neural networks. In Computer Aided Verification:
29th International Conference, CAV 2017, Heidelberg, Germany, July 24-28, 2017,
Proceedings, Part I 30, pages 97–117. Springer.
Katz, G., Huang, D. A., Ibeling, D., Julian, K., Lazarus, C., Lim, R., Shah, P., Thakoor, S.,
Wu, H., Zeljić, A., et al. (2019). The marabou framework for verification and analysis of
deep neural networks. In Computer Aided Verification: 31st International Conference,
CAV 2019, New York City, NY, USA, July 15-18, 2019, Proceedings, Part I 31, pages
443–452. Springer.
Khedr, H. and Shoukry, Y. (2023). Certifair: A framework for certified global fairness
of neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence,
volume 37, pages 8237–8245.
Kurakin, A., Goodfellow, I. J., and Bengio, S. (2018). Adversarial examples in the phys-
ical world. In Artificial intelligence safety and security, pages 99–112. Chapman and
Hall/CRC.
Lam, L. and Suen, S. (1997). Application of majority voting to pattern recognition: an
analysis of its behavior and performance. IEEE Transactions on Systems, Man, and
Cybernetics-Part A: Systems and Humans, 27(5):553–568.
Li, T., Xie, X., Wang, J., Guo, Q., Liu, A., Ma, L., and Liu, Y. (2023). Faire: Repair-
ing fairness of neural networks via neuron condition synthesis. ACM Transactions on
Software Engineering and Methodology, 33(1):1–24.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., Van
Der Laak, J. A., Van Ginneken, B., and Sánchez, C. I. (2017). A survey on deep learning
in medical image analysis. Medical image analysis, 42:60–88.
Ma, L., Juefei-Xu, F., Zhang, F., Sun, J., Xue, M., Li, B., Chen, C., Su, T., Li, L., Liu, Y.,
et al. (2018). Deepgauge: Multi-granularity testing criteria for deep learning systems.
In Proceedings of the 33rd ACM/IEEE international conference on automated software
engineering, pages 120–131.
Mohammadi, K., Sivaraman, A., and Farnadi, G. (2023). Feta: Fairness enforced verify-
ing, training, and predicting algorithms for neural networks. In Proceedings of the 3rd
ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization,
pages 1–11.
Pei, K., Cao, Y., Yang, J., and Jana, S. (2017). Deepxplore: Automated whitebox testing
of deep learning systems. In proceedings of the 26th Symposium on Operating Systems
Principles, pages 1–18.
Ruoss, A., Balunovic, M., Fischer, M., and Vechev, M. (2020). Learning certified in-
dividually fair representations. Advances in neural information processing systems,
33:7584–7596.
Saleiro, P., Kuester, B., Hinkson, L., London, J., Stevens, A., Anisfeld, A., Rodolfa, K. T.,
and Ghani, R. (2018). Aequitas: A bias and fairness audit toolkit. arXiv preprint
arXiv:1811.05577.
Sharma, A. and Wehrheim, H. (2020). Automatic fairness testing of machine learning
models. In Testing Software and Systems: 32nd IFIP WG 6.1 International Conference,
ICTSS 2020, Naples, Italy, December 9–11, 2020, Proceedings 32, pages 255–271.
Springer.
Singh, G., Gehr, T., Püschel, M., and Vechev, M. (2019). An abstract domain for certifying
neural networks. Proceedings of the ACM on Programming Languages, 3(POPL):1–30.
Sun, Y., Wu, M., Ruan, W., Huang, X., Kwiatkowska, M., and Kroening, D. (2018).
Concolic testing for deep neural networks. In Proceedings of the 33rd ACM/IEEE In-
ternational Conference on Automated Software Engineering, pages 109–119.
Udeshi, S., Arora, P., and Chattopadhyay, S. (2018). Automated directed fairness test-
ing. In Proceedings of the 33rd ACM/IEEE International Conference on Automated
Software Engineering, pages 98–108.
Urban, C., Christakis, M., Wüstholz, V., and Zhang, F. (2020). Perfectly parallel fairness
certification of neural networks. Proceedings of the ACM on Programming Languages,
4(OOPSLA):1–30.
Wang, S., Pei, K., Whitehouse, J., Yang, J., and Jana, S. (2018). Formal security analysis
of neural networks using symbolic intervals. In 27th USENIX Security Symposium
(USENIX Security 18), pages 1599–1614.
Yurochkin, M., Bower, A., and Sun, Y. (2019). Training individually fair ml models with
sensitive subspace robustness. arXiv preprint arXiv:1907.00020.
Zhang, J. M. and Harman, M. (2021). “ignorance and prejudice” in software fairness.
In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE),
pages 1436–1447. IEEE.
Zhang, L., Zhang, Y., and Zhang, M. (2021). Efficient white-box fairness testing through
gradient search. In Proceedings of the 30th ACM SIGSOFT International Symposium
on Software Testing and Analysis, pages 103–114.
Zhang, P., Wang, J., Sun, J., Dong, G., Wang, X., Wang, X., Dong, J. S., and Dai, T.
(2020). White-box fairness testing through adversarial sampling. In Proceedings of the
ACM/IEEE 42nd International Conference on Software Engineering, pages 949–960.
Zheng, H., Chen, Z., Du, T., Zhang, X., Cheng, Y., Ji, S., Wang, J., Yu, Y., and Chen,
J. (2022). Neuronfair: Interpretable white-box fairness testing through biased neuron
identification. In 44th International Conference on Software Engineering, pages 1–13,
New York, NY, USA. ACM. |
