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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 231240 of 971 papers

TitleStatusHype
Adapting the Biological SSVEP Response to Artificial Neural Networks0
Advancing Nearest Neighbor Explanation-by-Example with Critical Classification Regions0
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma0
Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI0
Designing explainable artificial intelligence with active inference: A framework for transparent introspection and decision-making0
Designing Explainable Predictive Machine Learning Artifacts: Methodology and Practical Demonstration0
Detecting AI-Generated Text in Educational Content: Leveraging Machine Learning and Explainable AI for Academic Integrity0
Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis0
AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Interpretable Models0
DLinear-based Prediction of Remaining Useful Life of Lithium-Ion Batteries: Feature Engineering through Explainable Artificial Intelligence0
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