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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
ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image RecognitionCode1
BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial IntelligenceCode1
TE2Rules: Explaining Tree Ensembles using RulesCode1
Towards ML Methods for Biodiversity: A Novel Wild Bee Dataset and Evaluations of XAI Methods for ML-Assisted Rare Species AnnotationsCode1
From Attribution Maps to Human-Understandable Explanations through Concept Relevance PropagationCode1
Towards Better Understanding Attribution MethodsCode1
Explainable Deep Learning Methods in Medical Image Classification: A SurveyCode1
A Song of (Dis)agreement: Evaluating the Evaluation of Explainable Artificial Intelligence in Natural Language ProcessingCode1
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language TasksCode1
XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine LearningCode1
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