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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 421430 of 971 papers

TitleStatusHype
Ensemble of Counterfactual ExplainersCode0
Acquiring Qualitative Explainable Graphs for Automated Driving Scene InterpretationCode0
CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures0
Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical DataCode0
Evaluating quantum generative models via imbalanced data classification benchmarks0
Deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: A systematic review0
Explainable AI for tool wear prediction in turning0
BSED: Baseline Shapley-Based Explainable Detector0
Explaining Black-Box Models through CounterfactualsCode1
DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System0
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