Programs as Black-Box Explanations

@article{Singh2016ProgramsAB,
  title={Programs as Black-Box Explanations},
  author={Sameer Singh and Marco T{\'u}lio Ribeiro and Carlos Guestrin},
  journal={CoRR},
  year={2016},
  volume={abs/1611.07579}
}
With increasing complexity of machine learning systems being used1, there is a crucial need for providing insights into what these models are doing. Model-agnostic approaches [18], such as Baehrens et al. [1] and Ribeiro et al. [17], have shown that insights into complex, black-box models do not have to come at a cost of accuracy, and that accurate local explanations can successfully be provided for a number of complex classifiers (such as random forests and deep neural networks) and domains… CONTINUE READING

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