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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 5160 of 971 papers

TitleStatusHype
Proposed Guidelines for the Responsible Use of Explainable Machine LearningCode1
Insights Into the Inner Workings of Transformer Models for Protein Function PredictionCode1
Landscape of R packages for eXplainable Artificial IntelligenceCode1
LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception TasksCode1
Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AICode1
Confident Teacher, Confident Student? A Novel User Study Design for Investigating the Didactic Potential of Explanations and their Impact on UncertaintyCode1
An Ensemble Framework for Explainable Geospatial Machine Learning ModelsCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept AlignmentCode1
Explainable Deep Learning Methods in Medical Image Classification: A SurveyCode1
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