Modèle d'apprentissage neuro-symbolique pour produire des prédictions interprétables pour de la classification d'imagines

Le résumé fourni par la source

Artificial Intelligence has been developing exponentially over the last decade. Its evolution is mainly linked to the progress of computer graphics card processors, allowing to accelerate the calculation of learning algorithms, and to the access to massive volumes of data. This progress has been principally driven by a search for quality prediction models, making them extremely accurate but opaque. Their large-scale adoption is hampered by their lack of transparency, thus causing the emergence of eXplainable Artificial Intelligence (XAI). This new line of research aims at fostering the use of learning models based on mass data by providing methods and concepts to obtain explanatory elements concerning their functioning. However, the youth of this field causes a lack of consensus and cohesion around the key definitions and objectives governing it. This thesis contributes to the field through two perspectives, one through a theory of what is XAI and how to achieve it and one practical. The first is based on a thorough review of the literature, resulting in two contributions: 1) the proposal of a new definition for Explainable Artificial Intelligence and 2) the creation of a new taxonomy of existing explainability methods. The practical contribution consists of two learning frameworks, both based on a paradigm aiming at linking the connectionist and symbolic paradigms.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.