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引用“”的文章Multi-Objective Counterfactual Explanations.

标题/作者	年份
Counterfactual explanations for machine learning: A review S Verma, J Dickerson, K Hines arXiv preprint arXiv:2010.10596	2020
Interpretable machine learning–a brief history, state-of-the-art and challenges C Molnar, G Casalicchio, B Bischl Joint European Conference on Machine Learning and Knowledge Discovery in …	2020
A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence I Stepin, JM Alonso, A Catala, M Pereira-Fariña IEEE Access 9, 11974-12001	2021
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects AH Karimi, G Barthe, B Schölkopf, I Valera arXiv preprint arXiv:2010.04050	2020
Explaining NLP models via minimal contrastive editing (MiCE) A Ross, A Marasović, ME Peters arXiv preprint arXiv:2012.13985	2020
If only we had better counterfactual explanations: Five key deficits to rectify in the evaluation of counterfactual xai techniques MT Keane, EM Kenny, E Delaney, B Smyth arXiv preprint arXiv:2103.01035	2021
Beyond individualized recourse: Interpretable and interactive summaries of actionable recourses K Rawal, H Lakkaraju Advances in Neural Information Processing Systems 33, 12187-12198	2020
Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications YL Chou, C Moreira, P Bruza, C Ouyang, J Jorge Information Fusion 81, 59-83	2022
Towards unifying feature attribution and counterfactual explanations: Different means to the same end R Kommiya Mothilal, D Mahajan, C Tan, A Sharma Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 652-663	2021
Counterfactual explainable recommendation J Tan, S Xu, Y Ge, Y Li, X Chen, Y Zhang Proceedings of the 30th ACM International Conference on Information …	2021
General pitfalls of model-agnostic interpretation methods for machine learning models C Molnar, G König, J Herbinger, T Freiesleben, S Dandl, CA Scholbeck, ... International Workshop on Extending Explainable AI Beyond Deep Models and …	2022
Carla: a python library to benchmark algorithmic recourse and counterfactual explanation algorithms M Pawelczyk, S Bielawski, J Heuvel, T Richter, G Kasneci arXiv preprint arXiv:2108.00783	2021
The intriguing relation between counterfactual explanations and adversarial examples T Freiesleben Minds and Machines 32 (1), 77-109	2022
Counterfactual explanation of machine learning survival models M Kovalev, L Utkin, F Coolen, A Konstantinov Informatica 32 (4), 817-847	2021
A causal perspective on meaningful and robust algorithmic recourse G König, T Freiesleben, M Grosse-Wentrup arXiv preprint arXiv:2107.07853	2021
Explainable machine learning with prior knowledge: an overview K Beckh, S Müller, M Jakobs, V Toborek, H Tan, R Fischer, P Welke, ... arXiv preprint arXiv:2105.10172	2021
Consequence-aware sequential counterfactual generation P Naumann, E Ntoutsi Joint European Conference on Machine Learning and Knowledge Discovery in …	2021
Explainable artificial intelligence for tabular data: A survey M Sahakyan, Z Aung, T Rahwan IEEE Access 9, 135392-135422	2021
A survey of algorithmic recourse: contrastive explanations and consequential recommendations AH Karimi, G Barthe, B Schölkopf, I Valera ACM Computing Surveys (CSUR)	2021
A survey on the robustness of feature importance and counterfactual explanations S Mishra, S Dutta, J Long, D Magazzeni arXiv preprint arXiv:2111.00358	2021

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