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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 631640 of 971 papers

TitleStatusHype
Explainable Reinforcement Learning on Financial Stock Trading using SHAP0
Explainable Artificial Intelligence for Assault Sentence Prediction in New Zealand0
An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain InjuryCode0
Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy0
A Means-End Account of Explainable Artificial Intelligence0
Multi-modal volumetric concept activation to explain detection and classification of metastatic prostate cancer on PSMA-PET/CTCode0
Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning0
An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model0
AI Approaches in Processing and Using Data in Personalized Medicine0
From Interpretable Filters to Predictions of Convolutional Neural Networks with Explainable Artificial Intelligence0
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