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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 361370 of 971 papers

TitleStatusHype
CNN-based explanation ensembling for dataset, representation and explanations evaluation0
Explainable Artificial Intelligence Techniques for Accurate Fault Detection and Diagnosis: A Review0
Explainable Artificial Intelligence Techniques for Irregular Temporal Classification of Multidrug Resistance Acquisition in Intensive Care Unit Patients0
Explainable Artificial Intelligence techniques for interpretation of food datasets: a review0
Explainable Artificial Intelligence Techniques for Software Development Lifecycle: A Phase-specific Survey0
Explainable Artificial Intelligence: a Systematic Review0
Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review0
Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection0
eXplainable Artificial Intelligence on Medical Images: A Survey0
Enhancing Cancer Diagnosis with Explainable & Trustworthy Deep Learning Models0
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