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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 771780 of 971 papers

TitleStatusHype
Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement0
Beyond explaining: XAI-based Adaptive Learning with SHAP Clustering for Energy Consumption Prediction0
Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives0
Beyond XAI:Obstacles Towards Responsible AI0
Biomarker Investigation using Multiple Brain Measures from MRI through XAI in Alzheimer's Disease Classification0
Breaking Down Financial News Impact: A Novel AI Approach with Geometric Hypergraphs0
Bridging Human Concepts and Computer Vision for Explainable Face Verification0
BSED: Baseline Shapley-Based Explainable Detector0
Multihop: Leveraging Complex Models to Learn Accurate Simple Models0
CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures0
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