Classifying Ethical AI Decisions with Explainable Prototype Based Deep Learning
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Document Type
Master Thesis
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CC-BY-NC-ND
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.
Keywords
machine ethics; social norms; machine learning; explainability; social computing; human-centered AI; responsible AI; online-learning