Sanity Checks Revisited: An Exploration to Repair the Model Parameter
  Randomisation Test
Anna Hedström Leander Weber Sebastian Lapuschkin Marina MC Höhne
The Model Parameter Randomisation Test (MPRT) is widely acknowledged in the
eXplainable Artificial Intelligence (XAI) community for its well-motivated
evaluative principle: that the explanation function should be sensitive to
changes in the parameters of the model function. However, recent works have
identified several methodological caveats for the empirical interpretation of
MPRT. To address these caveats, we introduce two adaptations to the original
MPRT -- Smooth MPRT and Efficient MPRT, where the former minimises the impact
that noise has on the evaluation results through sampling and the latter
circumvents the need for biased similarity measurements by re-interpreting the
test through the explanation's rise in complexity, after full parameter
randomisation. Our experimental results demonstrate that these proposed
variants lead to improved metric reliability, thus enabling a more trustworthy
application of XAI methods.