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Explainable Artificial Intelligence (XAI)

Explainable Artificial Intelligence

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Showing 526–550 of 1041 papers

TitleStatusHype
Overcoming Catastrophic Forgetting by XAI—0
Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF): A Data-Morphology-based Counterfactual Generation Method for Trustworthy Artificial Intelligence—0
Painting the black box white: experimental findings from applying XAI to an ECG reading setting—0
Echoes of Biases: How Stigmatizing Language Affects AI Performance—0
Pitfalls of Explainable ML: An Industry Perspective—0
Play MNIST For Me! User Studies on the Effects of Post-Hoc, Example-Based Explanations & Error Rates on Debugging a Deep Learning, Black-Box Classifier—0
Polynomial Threshold Functions of Bounded Tree-Width: Some Explainability and Complexity Aspects—0
Post-hoc explanation of black-box classifiers using confident itemsets—0
Precision of Individual Shapley Value Explanations—0
Predicting and explaining nonlinear material response using deep Physically Guided Neural Networks with Internal Variables—0
Predictive K-means with local models—0
Principles of Explanation in Human-AI Systems—0
Privacy Meets Explainability: A Comprehensive Impact Benchmark—0
Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing—0
Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review—0
Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI—0
Proceedings of the AI-HRI Symposium at AAAI-FSS 2020—0
Process Knowledge-infused Learning for Suicidality Assessment on Social Media—0
Process Knowledge-infused Learning for Clinician-friendly Explanations—0
Propagated Perturbation of Adversarial Attack for well-known CNNs: Empirical Study and its Explanation—0
Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems—0
Qualitative Investigation in Explainable Artificial Intelligence: A Bit More Insight from Social Science—0
Quality Metrics for Transparent Machine Learning With and Without Humans In the Loop Are Not Correlated—0
Revealing Similar Semantics Inside CNNs: An Interpretable Concept-based Comparison of Feature Spaces—0
Quantitative Evaluations on Saliency Methods: An Experimental Study—0
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