Classifying Ethical AI Decisions with Explainable Prototype Based Deep Learning

Abstract

We propose the NorMMo architecture, that integrates neuro-fuzzy classifiers to classify the social interpretation of some set of behavioral parameters in different social situations. This architecture can facilitate the classification of norm-complying or norm-violating behaviors for a social agent in an explainable and transparent manner.