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Found 7 papers, 5 papers with code
HOLMES: HOLonym-MEronym based Semantic inspection for Convolutional Image Classifiers
Convolutional Neural Networks (CNNs) are nowadays the model of choice in Computer Vision, thanks to their ability to automatize the feature extraction process in visual tasks.
Evaluating Link Prediction Explanations for Graph Neural Networks
Graph Machine Learning (GML) has numerous applications, such as node/graph classification and link prediction, in real-world domains.
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image Classifiers
Finally, we discuss how this approach can be further exploited in terms of explainability and adversarial robustness.
Streamlining models with explanations in the learning loop
Several explainable AI methods allow a Machine Learning user to get insights on the classification process of a black-box model in the form of local linear explanations.
GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs
Decoupling the feature space (edges) from a desired high-level explanation language (such as motifs) is thus a major challenge towards developing actionable explanations for graph classification tasks.
To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods
This highlights the need to have standard and unbiased evaluation procedures for Local Linear Explanations in the XAI field.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
FairLens: Auditing Black-box Clinical Decision Support Systems
The pervasive application of algorithmic decision-making is raising concerns on the risk of unintended bias in AI systems deployed in critical settings such as healthcare.
