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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 6170 of 971 papers

TitleStatusHype
Guidelines and Evaluation of Clinical Explainable AI in Medical Image AnalysisCode1
ContrXT: Generating Contrastive Explanations from any Text ClassifierCode1
Gaussian Process Regression With Interpretable Sample-Wise Feature WeightsCode1
Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in AutismCode1
Counterfactual Shapley Additive ExplanationsCode1
Explaining deep learning models for spoofing and deepfake detection with SHapley Additive exPlanationsCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
Consistent Explanations by Contrastive LearningCode1
Focus! Rating XAI Methods and Finding BiasesCode1
Logic Explained NetworksCode1
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