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.
@article{arxiv.1906.06566,
title = {LioNets: Local Interpretation of Neural Networks through Penultimate Layer Decoding},
author = {Ioannis Mollas and Nikolaos Bassiliades and Grigorios Tsoumakas},
journal= {arXiv preprint arXiv:1906.06566},
year = {2021}
}