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Found 6 papers, 0 papers with code
On the Multiple Roles of Ontologies in Explainable AI
This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and intelligible explanations.
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions
As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Predicting rice blast disease: machine learning versus process based models
In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and WARM) and two approaches based on machine learning algorithms (M5Rules and RNN), the former inducing a rule-based model and the latter building a neural network.
Trepan Reloaded: A Knowledge-driven Approach to Explaining Artificial Neural Networks
Whilst a plethora of approaches have been developed for post-hoc explainability, only a few focus on how to use domain knowledge, and how this influences the understandability of global explanations from the users' perspective.
Repairing Ontologies via Axiom Weakening
Ontology engineering is a hard and error-prone task, in which small changes may lead to errors, or even produce an inconsistent ontology.
An Argument-based Creative Assistant for Harmonic Blending
This tool allows a music expert to specify arguments over given transition properties.
