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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 331340 of 971 papers

TitleStatusHype
Detecting mental disorder on social media: a ChatGPT-augmented explainable approachCode0
NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble TechniquesCode0
Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence0
Unveiling Molecular Moieties through Hierarchical Grad-CAM Graph ExplainabilityCode0
Emergent Explainability: Adding a causal chain to neural network inference0
Deep Learning for Gamma-Ray Bursts: A data driven event framework for X/Gamma-Ray analysis in space telescopesCode1
XAI for All: Can Large Language Models Simplify Explainable AI?0
Automated facial recognition system using deep learning for pain assessment in adults with cerebral palsy0
REVEX: A Unified Framework for Removal-Based Explainable Artificial Intelligence in VideoCode0
Assessing the Efficacy of Deep Learning Approaches for Facial Expression Recognition in Individuals with Intellectual Disabilities0
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