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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 611620 of 971 papers

TitleStatusHype
Transferability and explainability of deep learning emulators for regional climate model projections: Perspectives for future applications0
Transfer Learning and Explainable AI for Brain Tumor Classification: A Study Using MRI Data from Bangladesh0
Transferring Domain Knowledge with (X)AI-Based Learning Systems0
Leveraging Expert Input for Robust and Explainable AI-Assisted Lung Cancer Detection in Chest X-rays0
Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy0
Trustworthy XAI and Application0
Tuning for Trustworthiness -- Balancing Performance and Explanation Consistency in Neural Network Optimization0
Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond0
Uncertainty Quantification of Wind Gust Predictions in the Northeast US: An Evidential Neural Network and Explainable Artificial Intelligence Approach0
Vehicle Fuel Optimization Under Real-World Driving Conditions: An Explainable Artificial Intelligence Approach0
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