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Explainable AI has attracted much research attention in recent years with feature attribution algorithms, which compute "feature importance" in predictions, becoming increasingly popular.
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Rathi, S.: Generating counterfactual and contrastive explanations using SHAP. CoRR abs/1906.09293
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Ribeiro, M.T., Singh, S., Guestrin, C.: ”why should I trust you?”: Explaining the predictions of any classifier. In: Proc. of SIGKDD. pp. 1135–1144 (2016)
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Ribeiro, M.T., Singh, S., Guestrin, C.: ”why should I trust you?”: Explaining the predictions of any classifier. In: Proc. of KDD. pp. 1135–1144. ACM (2016). https://doi.org/10.1145/2939672.2939778, https://doi.org/10.1145/2939672.2939778
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Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access 6
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Alonso, J., Ramos Soto, A., Castiello, C., Mencar, C.: Hybrid data-expert explainable beer style classifier. In: Proc. of IJCAI-17 Workshop on Explainable AI (2018)
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Silva, W., Fernandes, K., Cardoso, M.J., Cardoso, J.S.: Towards complementary explanations using deep neural networks. In: Proc. of MLCN. vol. 11038, pp. 133–140. Springer (2018). https://doi.org/10.1007/978-3-030-02628-8_15, https://doi.org/10.1007/978-3-030-02628-8_15
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Arrieta, A.B., Rodríguez, N.D., Ser, J.D., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., Herrera, F.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58
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Robnik-Sikonja, M., Bohanec, M.: Perturbation-based explanations of prediction models. In: Human and Machine Learning - Visible, Explainable, Trustworthy and Transparent, pp. 159–175. Springer (2018)
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Shih, A., Choi, A., Darwiche, A.: A symbolic approach to explaining bayesian network classifiers. In: Proc. of IJCAI. pp. 5103–5111 (2018)
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Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: adversarial attacks on post hoc explanation methods. In: Markham, A.N., Powles, J., Walsh, T., Washington, A.L. (eds.) Proc. of AIES. pp. 180–186. ACM (2020). https://doi.org/10.1145/3375627.3375830, https://doi.org/10.1145/3375627.3375830
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