Deep Neural Networks; Black Box Problem; XAI; explanation; understanding; scientific models
Résumé :
[en] What Deep Neural Networks (DNNs) can do is impressive, yet they are notoriously opaque. Responding to the worries associated with this opacity, the field of XAI has produced a plethora of methods purporting to explain the workings of DNNs. Unsurprisingly, a whole host of questions revolves around the notion of explanation central to this field. This note provides a roadmap of the recent work that tackles these questions from the perspective of philosophical ideas on explanations and models in science.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
KNOKS, Aleks ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
RALEIGH, Thomas ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Humanities (DHUM)
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
XAI and philosophical work on explanation: A roadmap
Date de publication/diffusion :
2022
Nom de la manifestation :
1st Workshop on Bias, Ethical AI, Explainability and the Role of Logic and Logic Programming
Organisateur de la manifestation :
University of Udine
Lieu de la manifestation :
Udine, Italie
Date de la manifestation :
2-12-2022
Manifestation à portée :
International
Titre du périodique :
Proceedings of 1st Workshop on Bias, Ethical AI, Explainability and the Role of Logic and Logic Programming
eISSN :
1613-0073
Maison d'édition :
CEUR-WS.org
Titre particulier du numéro :
Proceedings of 1st Workshop on Bias, Ethical AI, Explainability and the Role of Logic and Logic Programming
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