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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 161170 of 971 papers

TitleStatusHype
Coherent Local Explanations for Mathematical OptimizationCode0
Uncertainty Quantification of Wind Gust Predictions in the Northeast US: An Evidential Neural Network and Explainable Artificial Intelligence Approach0
Concept-Based Explainable Artificial Intelligence: Metrics and Benchmarks0
Towards Transparent and Accurate Diabetes Prediction Using Machine Learning and Explainable Artificial Intelligence0
Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI AssistantCode0
Explainable Artificial Intelligence for identifying profitability predictors in Financial Statements0
Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks0
Towards Explainable Multimodal Depression Recognition for Clinical InterviewsCode0
Ensuring Medical AI Safety: Explainable AI-Driven Detection and Mitigation of Spurious Model Behavior and Associated DataCode0
Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review0
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