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Found 4 papers, 3 papers with code
Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression
Deep Neural Networks are prone to learning and relying on spurious correlations in the training data, which, for high-risk applications, can have fatal consequences.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Manipulating Feature Visualizations with Gradient Slingshots
Deep Neural Networks (DNNs) are capable of learning complex and versatile representations, however, the semantic nature of the learned concepts remains unknown.
Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science
We find architecture-dependent performance differences regarding robustness, complexity and localization skills of different XAI methods, highlighting the necessity for research task-specific evaluation.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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.
