Corpus ID: 3652280

How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation

@article{Narayanan2018HowDH,
  title={How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation},
  author={M. Narayanan and Emily Chen and Jeffrey He and Been Kim and Sam Gershman and Finale Doshi-Velez},
  journal={ArXiv},
  year={2018},
  volume={abs/1902.00006}
}
  • M. Narayanan, Emily Chen, +3 authors Finale Doshi-Velez
  • Published 2018
  • Computer Science, Mathematics
  • ArXiv
  • Recent years have seen a boom in interest in machine learning systems that can provide a human-understandable rationale for their predictions or decisions. However, exactly what kinds of explanation are truly human-interpretable remains poorly understood. This work advances our understanding of what makes explanations interpretable in the specific context of verification. Suppose we have a machine learning system that predicts X, and we provide rationale for this prediction X. Given an input… CONTINUE READING
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