Local explanation approaches focus on highlighting a handful of crucial features (Baehrens et al., 2010) or deriving simpler, more readable models from a complex one, e.g., a binary decision tree (Frosst and Hinton, 2017), or by local approximation with linear models (Ribeiro et al., 2016).
although they can explicitly show the representations learned in the specific hidden neurons (Frosst and Hinton, 2017), these approaches base their effectiveness on the user ability to study the quality of the reasoning and of the accountability as a side effect of the quality and the coherence of the features selection: this can be very hard in tasks where boundaries between classes are not well defined.