Neural Network eXplainable AI Based on Paraconsistent Analysis - an Initial Approach

Francisco Supino Marcondes, Dalila Durães, Marco Gomes, Flávio Arthur O. Santos, José João Almeida, Paulo Novais

Abstract: Para-consistent logic is a non-classical logic whose foundations allow the treatment of contradictions without invalidating the conclusions. This paper presented an attempt of using Annotated Para-consistent Analysis (APA) for supporting eXplained Artificial Intelligence (XAI) on Neural-Networks. For the study case, it was presented a situation where a binary classification model was able to correctly recognize one label but not the other with the form of the principle of explosion (p\(\wedge \lnot \)p). By plotting the linear output max-min scaled data into the paraconsistent reticulate, it was possible to properly understand that the selected architectures were being able to predict. This result is early support for using APA for Neural-Network XAI, corroborating with this paper hypothesis.