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Found 6 papers, 1 papers with code
CoLa-DCE -- Concept-guided Latent Diffusion Counterfactual Explanations
The counterfactuals comprise an increased granularity through minimal feature changes.
Locally Testing Model Detections for Semantic Global Concepts
While global concept encodings generally enable a user to test a model for a specific concept, linking global concept encodings to the local processing of single network inputs reveals their strengths and limitations.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
The Anatomy of Adversarial Attacks: Concept-based XAI Dissection
Adversarial attacks (AAs) pose a significant threat to the reliability and robustness of deep neural networks.
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models.
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond
The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to systematically review and compare explanation methods in order to confirm their correctness.
Measurably Stronger Explanation Reliability via Model Canonization
While rule-based attribution methods have proven useful for providing local explanations for Deep Neural Networks, explaining modern and more varied network architectures yields new challenges in generating trustworthy explanations, since the established rule sets might not be sufficient or applicable to novel network structures.
