  Machine learning methods are being increasingly applied in sensitive societal
contexts, where decisions impact human lives. Hence it has become necessary to
build capabilities for providing easily-interpretable explanations of models'
predictions. Recently in academic literature, a vast number of explanations
methods have been proposed. Unfortunately, to our knowledge, little has been
documented about the challenges machine learning practitioners most often face
when applying them in real-world scenarios. For example, a typical procedure
such as feature engineering can make some methodologies no longer applicable.
The present case study has two main objectives. First, to expose these
challenges and how they affect the use of relevant and novel explanations
methods. And second, to present a set of strategies that mitigate such
challenges, as faced when implementing explanation methods in a relevant
application domain -- poverty estimation and its use for prioritizing access to
social policies.
