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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 271280 of 971 papers

TitleStatusHype
Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI0
CNN-based explanation ensembling for dataset, representation and explanations evaluation0
Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression0
Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives0
Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational dataCode0
Unraveling the Dilemma of AI Errors: Exploring the Effectiveness of Human and Machine Explanations for Large Language Models0
Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis0
Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI0
Interpreting End-to-End Deep Learning Models for Speech Source Localization Using Layer-wise Relevance PropagationCode0
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey0
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