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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 311320 of 971 papers

TitleStatusHype
Explainable AI for Embedded Systems Design: A Case Study of Static Redundant NVM Memory Write Prediction0
An Ensemble Framework for Explainable Geospatial Machine Learning ModelsCode1
XAI-Based Detection of Adversarial Attacks on Deepfake DetectorsCode0
AcME-AD: Accelerated Model Explanations for Anomaly Detection0
Introducing User Feedback-based Counterfactual Explanations (UFCE)Code0
Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity0
Position: Explain to Question not to Justify0
LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception TasksCode1
MultiFIX: An XAI-friendly feature inducing approach to building models from multimodal data0
Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive BiasCode0
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