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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

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Showing 401–425 of 971 papers

TitleStatusHype
Research on Older Adults' Interaction with E-Health Interface Based on Explainable Artificial Intelligence—0
Linguistically Communicating Uncertainty in Patient-Facing Risk Prediction Models—0
Detecting mental disorder on social media: a ChatGPT-augmented explainable approachCode0
NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble TechniquesCode0
Bridging Human Concepts and Computer Vision for Explainable Face Verification—0
Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence—0
Unveiling Molecular Moieties through Hierarchical Grad-CAM Graph ExplainabilityCode0
Emergent Explainability: Adding a causal chain to neural network inference—0
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 palsy—0
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 Disabilities—0
Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree EnsemblesCode0
A comprehensive study on fidelity metrics for XAICode0
Eclectic Rule Extraction for Explainability of Deep Neural Network based Intrusion Detection Systems—0
Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective—0
Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis—0
Manifold-based Shapley for SAR Recognization Network Explanation—0
XXAI: Towards eXplicitly eXplainable Artificial Intelligence—0
Concept-based Explainable Artificial Intelligence: A Survey—0
Explainable artificial intelligence approaches for brain-computer interfaces: a review and design spaceCode0
Explainable Multimodal Sentiment Analysis on Bengali Memes—0
Locally-Minimal Probabilistic ExplanationsCode0
CAManim: Animating end-to-end network activation maps—0
An Interpretable Deep Learning Approach for Skin Cancer CategorizationCode0
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