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Causal Interpretability for Machine Learning – Problems, Methods and Evaluation
[article]
2020
arXiv
pre-print
Machine learning models have had discernible achievements in a myriad of applications. However, most of these models are black-boxes, and it is obscure how the decisions are made by them. This makes the models unreliable and untrustworthy. To provide insights into the decision making processes of these models, a variety of traditional interpretable models have been proposed. Moreover, to generate more human-friendly explanations, recent work on interpretability tries to answer questions related
arXiv:2003.03934v3
fatcat:awzv47nmv5aqtl4j5asmp5v7zq