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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 2130 of 971 papers

TitleStatusHype
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
Confident Teacher, Confident Student? A Novel User Study Design for Investigating the Didactic Potential of Explanations and their Impact on UncertaintyCode1
Axiomatic Attribution for Deep NetworksCode1
ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformersCode1
In-Context Explainers: Harnessing LLMs for Explaining Black Box ModelsCode1
Driving Behavior Explanation with Multi-level FusionCode1
Calibrated Explanations for RegressionCode1
A Song of (Dis)agreement: Evaluating the Evaluation of Explainable Artificial Intelligence in Natural Language ProcessingCode1
Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in AutismCode1
A Fresh Look at Sanity Checks for Saliency MapsCode1
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