  Machine learning models are used in many sensitive areas where besides
predictive accuracy their comprehensibility is also important. Interpretability
of prediction models is necessary to determine their biases and causes of
errors, and is a necessary prerequisite for users' confidence. For complex
state-of-the-art black-box models post-hoc model-independent explanation
techniques are an established solution. Popular and effective techniques, such
as IME, LIME, and SHAP, use perturbation of instance features to explain
individual predictions. Recently, Slack et al. (2020) put their robustness into
question by showing that their outcomes can be manipulated due to poor
perturbation sampling employed. This weakness would allow dieselgate type
cheating of owners of sensitive models who could deceive inspection and hide
potentially unethical or illegal biases existing in their predictive models.
This could undermine public trust in machine learning models and give rise to
legal restrictions on their use.
  We show that better sampling in these explanation methods prevents malicious
manipulations. The proposed sampling uses data generators that learn the
training set distribution and generate new perturbation instances much more
similar to the training set. We show that the improved sampling increases the
robustness of the LIME and SHAP, while previously untested method IME is
already the most robust of all.
