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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 7180 of 971 papers

TitleStatusHype
In-Context Explainers: Harnessing LLMs for Explaining Black Box ModelsCode1
Driving Behavior Explanation with Multi-level FusionCode1
Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AICode1
Entropy-based Logic Explanations of Neural NetworksCode1
Explainable AI for Bioinformatics: Methods, Tools, and ApplicationsCode1
Extending CAM-based XAI methods for Remote Sensing Imagery SegmentationCode1
Explainable Earth Surface Forecasting under Extreme EventsCode1
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local ExplanationsCode1
Extracting human interpretable structure-property relationships in chemistry using XAI and large language modelsCode1
Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2Code1
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