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arXiv 1911.07749cs.LGcs.AIstat.ML

On the computation of counterfactual explanations -- A survey

Bielefeld University(比勒费尔德大学)

André Artelt, Barbara Hammer

英文摘要:

Due to the increasing use of machine learning in practice it becomes more and more important to be able to explain the prediction and behavior of machine learning models. An instance of explanations are counterfactual explanations which provide an intuitive and useful explanations of machine learning models. In this survey we review model-specific methods for efficiently computing counterfactual explanations of many different machine learning models and propose methods for models that have not been considered in literature so far.

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