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Explainable artificial intelligence

XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.

Papers

Showing 481490 of 971 papers

TitleStatusHype
Motif-guided Time Series Counterfactual Explanations0
MultiFIX: An XAI-friendly feature inducing approach to building models from multimodal data0
Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer0
Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions0
Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models0
Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows0
Natural Example-Based Explainability: a Survey0
Neural network interpretability with layer-wise relevance propagation: novel techniques for neuron selection and visualization0
Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network0
nn2poly: An R Package for Converting Neural Networks into Interpretable Polynomials0
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