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Machine learning (ML) models, demonstrably powerful, suffer from a lack of interpretability.
Model explanations with differential privacy,
N. Patel, R. Shokri, Y. Zick, · 1904
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Explaining and harnessing adversarial examples,
I. J. Goodfellow, J. Shlens, C. Szegedy, · 2014
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Machine learning: Trends, perspectives, and prospects,
M. I. Jordan, T. M. Mitchell, · 2015
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation,
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, W. Samek, · 2015
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” why should i trust you?” explaining the predictions of any classifier,
M. T. Ribeiro, S. Singh, C. Guestrin, · 2016
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Stealing machine learning models via prediction
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, T. Ristenpart, · 2016
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Explainable artificial intelligence (xai),
D. Gunning, · 2017
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Axiomatic attribution for deep networks,
M. Sundararajan, A. Taly, Q. Yan, · 2017
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Learning important features through propagating activation differences,
A. Shrikumar, P. Greenside, A. Kundaje, · 2017
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A unified approach to interpreting model predictions,
S. M. Lundberg, S.-I. Lee, · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the gdpr,
S. Wachter, B. Mittelstadt, C. Russell, · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization,
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, · 2017
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Interpretable predictions of tree-based ensembles via actionable feature tweaking,
G. Tolomei, F. Silvestri, A. Haines, M. Lalmas, · 2017
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Membership inference attacks against machine learning models,
R. Shokri, M. Stronati, C. Song, V. Shmatikov, · 2017
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The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery.,
Z. C. Lipton, · 2018
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General data protection regulation,
P. Regulation, · 2018
Cited alongside, same era.
Informational privacy, a right to explanation, and interpretable ai,
T. W. Kim, B. R. Routledge, · 2018
Cited alongside, same era.
Explanations based on the missing: Towards contrastive explanations with pertinent negatives,
A. Dhurandhar, P.-Y. Chen, R. Luss, C.-C. Tu, P. Ting, K. Shanmugam, P. Das, · 2018
Cited alongside, same era.
Causability and explainability of artificial intelligence in medicine,
A. Holzinger, G. Langs, H. Denk, K. Zatloukal, H. Müller, · 2019
Cited alongside, same era.
Actionable recourse in linear classification,
B. Ustun, A. Spangher, Y. Liu, · 2019
Cited alongside, same era.
Explaining deep learning models with constrained adversarial examples,
J. Moore, N. Hammerla, C. Watkins, · 2019
Cited alongside, same era.
Ordered counterfactual explanation by mixed-integer linear optimization,
K. Kanamori, T. Takagi, K. Kobayashi, Y. Ike, K. Uemura, H. Arimura, · 2021
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Counterfactual explanation with multi-agent reinforcement learning for drug target prediction,
T. M. Nguyen, T. P. Quinn, T. Nguyen, T. Tran, · 2021
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Fall of giants: How popular text-based mlaas fall against a simple evasion attack,
L. Pajola, M. Conti, · 2021
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Megex: Data-free model extraction attack against gradient-based explainable ai,
T. Miura, S. Hasegawa, T. Shibahara, · 2021
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Exploiting explanations for model inversion attacks,
X. Zhao, W. Zhang, X. Xiao, B. Lim, · 2021
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White-box vs black-box: Bayes optimal strategies for membership inference,
A. Sablayrolles, M. Douze, C. Schmid, Y. Ollivier, H. Jégou, · 2019
Cited alongside, same era.
Model reconstruction from model explanations,
S. Milli, L. Schmidt, A. D. Dragan, M. Hardt, · 2019
Cited alongside, same era.
A survey of privacy attacks in machine learning,
M. Rigaki, S. Garcia, · 2020
Cited alongside, same era.
Working in contexts for which transparency is important: A recordkeeping view of explainable artificial intelligence (xai),
J. Bunn, · 2020
Cited alongside, same era.
Counterfactual explanations and algorithmic recourses for machine learning: A review,
S. Verma, V. Boonsanong, M. Hoang, K. E. Hines, J. P. Dickerson, C. Shah, · 2020
Cited alongside, same era.
Scout: Self-aware discriminant counterfactual explanations,
P. Wang, N. Vasconcelos, · 2020
Cited alongside, same era.
A review of taxonomies of explainable artificial intelligence (xai) methods,
T. Speith, · 2022
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Towards explainable model extraction attacks,
A. Yan, R. Hou, X. Liu, H. Yan, T. Huang, X. Wang, · 2022
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Dualcf: Efficient model extraction attack from counterfactual explanations,
Y. Wang, H. Qian, C. Miao, · 2022
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Label-only model inversion attacks: Attack with the least information,
T. Zhu, D. Ye, S. Zhou, B. Liu, W. Zhou, · 2022
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Learning to generate inversion-resistant model explanations,
H. Jeong, S. Lee, S. J. Hwang, S. Son, · 2022
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The privacy issue of counterfactual explanations: explanation linkage attacks,
S. Goethals, K. Sörensen, D. Martens, · 2023
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Sac-fact: Soft actor-critic reinforcement learning for counterfactual explanations,
F. Ezzeddine, O. Ayoub, D. Andreoletti, S. Giordano, · 2023
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A. C. Oksuz, A. Halimi, E. Ayday, · 2023
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Vertical split learning-based identification and explainable deep learning-based localization of failures in multi-domain nfv systems,
F. Ezzeddine, O. Ayoub, D. Andreoletti, M. Tornatore, S. Giordano, · 2023
Later among the works it cites.
Netguard: Protecting commercial web apis from model inversion attacks using gan-generated fake samples,
X. Gong, Z. Wang, Y. Chen, Q. Wang, C. Wang, C. Shen, · 2053
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