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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 241250 of 971 papers

TitleStatusHype
Explaining Expert Search and Team Formation Systems with ExES0
A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation0
Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF): A Data-Morphology-based Counterfactual Generation Method for Trustworthy Artificial Intelligence0
EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods0
From SHAP Scores to Feature Importance Scores0
Empowering Prior to Court Legal Analysis: A Transparent and Accessible Dataset for Defensive Statement Classification and Interpretation0
Tell me more: Intent Fulfilment Framework for Enhancing User Experiences in Conversational XAI0
Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation0
Challenges and Opportunities in Text Generation Explainability0
Evaluating the Explainable AI Method Grad-CAM for Breath Classification on Newborn Time Series Data0
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