A Novel Integration of Data-Driven Rule Generation and Computational Argumentation for Enhanced Explainable AI
Abstract Explainable Artificial Intelligence (XAI) is a research area that clarifies AI decision-
making processes to build user trust and promote responsible AI. Hence, a key scientific …
making processes to build user trust and promote responsible AI. Hence, a key scientific …
A global model-agnostic rule-based XAI method based on Parameterized Event Primitives for time series classifiers
Time series classification is a challenging research area where machine learning and deep
learning techniques have shown remarkable performance. However, often, these are seen …
learning techniques have shown remarkable performance. However, often, these are seen …
Exploring Commonalities in Explanation Frameworks: A Multi-Domain Survey Analysis
This study presents insights gathered from surveys and discussions with specialists in three
domains, aiming to find essential elements for a universal explanation framework that could …
domains, aiming to find essential elements for a universal explanation framework that could …
SRFAMap: A Method for Mapping Integrated Gradients of a CNN Trained with Statistical Radiomic Features to Medical Image Saliency Maps
Many explainable AI methods for generating medical image saliency maps exist, but most
are devoted to working on trained neural network-based models. At the same time, many …
are devoted to working on trained neural network-based models. At the same time, many …
[PDF][PDF] A Novel Model-Agnostic xAI Method Guided by Cost-Sensitive Tree Models and Argumentative Decision Graphs
M Kopanja - 2024 - ceur-ws.org
In recent years there is increasing demand for comprehension and explainability of the
inferences machine learning (ML) models make. Many explainable artificial intelligence …
inferences machine learning (ML) models make. Many explainable artificial intelligence …