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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 261270 of 971 papers

TitleStatusHype
An Explainable and Conformal AI Model to Detect Temporomandibular Joint Involvement in Children Suffering from Juvenile Idiopathic Arthritis0
Towards trustable SHAP scores0
Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle0
Fiper: a Visual-based Explanation Combining Rules and Feature Importance0
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients0
Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments0
How should AI decisions be explained? Requirements for Explanations from the Perspective of European Law0
Towards Robust Ferrous Scrap Material Classification with Deep Learning and Conformal Prediction0
Concept Induction using LLMs: a user experiment for assessment0
Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI0
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