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Machine learning techniques are increasingly used for high-stakes decision-making, such as college admissions, loan attribution or recidivism prediction.
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2022
Later among the works it cites.
T. Datta, D. Nissani, M. Cembalest, A. Khanna, H. Massa, and J. P. Dickerson, “Position: Tensions between the proxies of human values in AI,” in First IEEE Conference on Secure and Trustworthy Machine Learning , 2023. [Online]. Available: https://openreview.net/forum?id=7EjikkMkIl
2023
Closest in time.
J. Schöffer, “On the interplay of transparency and fairness in ai-informed decision-making,” 2023
2023
Closest in time.
S. Caton and C. Haas, “Fairness in machine learning: A survey,” ACM Comput. Surv. , aug 2023. [Online]. Available: https://doi.org/10.1145/3616865
2023
Closest in time.
M. Rigaki and S. Garcia, “A survey of privacy attacks in machine learning,” ACM Comput. Surv. , sep 2023. [Online]. Available: https://doi.org/10.1145/3624010
2023
Closest in time.
A. Karimi, G. Barthe, B. Schölkopf, and I. Valera, “A survey of algorithmic recourse: Contrastive explanations and consequential recommendations,” ACM Comput. Surv. , vol. 55, no. 5, pp. 95:1–95:29, 2023. [Online]. Available: https://doi.org/10.1145/3527848
2023
Closest in time.
gabriel laberge, U. Aïvodji, S. Hara, M. Marchand, and F. Khomh, “Fooling SHAP with stealthily biased sampling,” in The Eleventh International Conference on Learning Representations , 2023. [Online]. Available: https://openreview.net/forum?id=J4mJjotSauh
2023
Closest in time.
J. Ferry, U. Aïvodji, S. Gambs, M.-J. Huguet, and M. Siala, “Exploiting fairness to enhance sensitive attributes reconstruction,” in First IEEE Conference on Secure and Trustworthy Machine Learning , 2023. [Online]. Available: https://openreview.net/forum?id=tOVr0HLaFz0
2023
Closest in time.
K. Koch and M. Soll, “No matter how you slice it: Machine unlearning with SISA comes at the expense of minority classes,” in First IEEE Conference on Secure and Trustworthy Machine Learning , 2023. [Online]. Available: https://openreview.net/forum?id=RBX1H-SGdT
2023
Closest in time.
P. Mangold, M. Perrot, A. Bellet, and M. Tommasi, “Differential privacy has bounded impact on fairness in classification,” in International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA , ser. Proceedings of Machine Learning Research, A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, and J. Scarlett, Eds., vol. 202. PMLR, 2023, pp. 23 681–23 705. [Online]. Available: https://proceedings.mlr.press/v202/mangold23a.html
2023
Closest in time.
R. Friedberg and R. Rogers, “Privacy aware experimentation over sensitive groups: A general chi square approach,” in Workshop on Algorithmic Fairness through the Lens of Causality and Privacy . PMLR, 2023, pp. 23–66
2023
Closest in time.
2023
Closest in time.
T. D. T. Nguyen, P. Lai, H. Phan, and M. T. Thai, “Xrand: Differentially private defense against explanation-guided attacks,” in Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, IAAI 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, February 7-14, 2023 , B. Williams, Y. Chen, and J. Neville, Eds. AAAI Press, 2023, pp. 11 873–11 881. [Online]. Available: https://doi.org/10.1609/aaai.v37i10.26401
2023
Closest in time.