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Understanding black-box predictions via influence functions. arXiv preprint arXiv:1703.04730 ( 2017 ). Pang Wei Koh and Percy Liang. 2017. Understanding black-box predictions via influence functions. arXiv preprint arXiv:1703.04730 (2017)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101820"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2520149"},{"key":"e_1_3_2_1_31_1","unstructured":"Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Advances in neural information processing systems. 4765--4774.  Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. 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