Classifying Ethical AI Decisions with Explainable Prototype Based Deep Learning

dc.rights.license	CC-BY-NC-ND
dc.contributor.advisor	Dell'Anna, Davide
dc.contributor.author	Lindonk, Mara van
dc.date.accessioned	2025-06-27T11:00:48Z
dc.date.available	2025-06-27T11:00:48Z
dc.date.issued	2025
dc.identifier.uri	https://studenttheses.uu.nl/handle/20.500.12932/49065
dc.description.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.
dc.description.sponsorship	Utrecht University
dc.language.iso	EN
dc.subject	We propose the NorMMo architecture that can be used for the classification of normative behavior in an explainable, transparent and efficient manner.
dc.title	Classifying Ethical AI Decisions with Explainable Prototype Based Deep Learning
dc.type.content	Master Thesis
dc.rights.accessrights	Open Access
dc.subject.keywords	machine ethics; social norms; machine learning; explainability; social computing; human-centered AI; responsible AI; online-learning
dc.subject.courseuu	Artificial Intelligence
dc.thesis.id	46787