Nature Communications (Mar 2019)

Unmasking Clever Hans predictors and assessing what machines really learn

  • Sebastian Lapuschkin,
  • Stephan Wäldchen,
  • Alexander Binder,
  • Grégoire Montavon,
  • Wojciech Samek,
  • Klaus-Robert Müller

DOI
https://doi.org/10.1038/s41467-019-08987-4
Journal volume & issue
Vol. 10, no. 1
pp. 1 – 8

Abstract

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Nonlinear machine learning methods have good predictive ability but the lack of transparency of the algorithms can limit their use. Here the authors investigate how these methods approach learning in order to assess the dependability of their decision making.