  Technological breakthroughs on smart homes, self-driving cars, health care
and robotic assistants, in addition to reinforced law regulations, have
critically influenced academic research on explainable machine learning. A
sufficient number of researchers have implemented ways to explain indifferently
any black box model for classification tasks. A drawback of building agnostic
explanators is that the neighbourhood generation process is universal and
consequently does not guarantee true adjacency between the generated neighbours
and the instance. This paper explores a methodology on providing explanations
for a neural network's decisions, in a local scope, through a process that
actively takes into consideration the neural network's architecture on creating
an instance's neighbourhood, that assures the adjacency among the generated
neighbours and the instance.
