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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
Using agent-based models and EXplainable Artificial Intelligence (XAI) to simulate social behaviors and policy intervention scenarios: A case study of private well users in Ireland0
Using Deep Learning and Explainable Artificial Intelligence in Patients' Choices of Hospital Levels0
Using explainability to design physics-aware CNNs for solving subsurface inverse problems0
Using Explainable AI to Cross-Validate Socio-economic Disparities Among Covid-19 Patient Mortality0
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model0
Utilizing Explainable AI for improving the Performance of Neural Networks0
Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)0
Vector symbolic architectures for context-free grammars0
Visual Explanations with Attributions and Counterfactuals on Time Series Classification0
Visualizing the Diversity of Representations Learned by Bayesian Neural Networks0
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