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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 276–300 of 971 papers

TitleStatusHype
Unraveling the Dilemma of AI Errors: Exploring the Effectiveness of Human and Machine Explanations for Large Language Models—0
Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis—0
Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI—0
Interpreting End-to-End Deep Learning Models for Speech Source Localization Using Layer-wise Relevance PropagationCode0
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey—0
X-SHIELD: Regularization for eXplainable Artificial Intelligence—0
Automatic Extraction of Linguistic Description from Fuzzy Rule BaseCode1
Procedural Fairness in Machine LearningCode0
Energy-based Model for Accurate Shapley Value Estimation in Interpretable Deep Learning Predictive ModelingCode0
Explainable AI Integrated Feature Engineering for Wildfire Prediction—0
Automatic explanation of the classification of Spanish legal judgments in jurisdiction-dependent law categories with tree estimators—0
Leveraging Counterfactual Paths for Contrastive Explanations of POMDP Policies—0
Leveraging Expert Input for Robust and Explainable AI-Assisted Lung Cancer Detection in Chest X-rays—0
Clinical Domain Knowledge-Derived Template Improves Post Hoc AI Explanations in Pneumothorax ClassificationCode0
Intrinsic Subgraph Generation for Interpretable Graph based Visual Question AnsweringCode0
The Anatomy of Adversarial Attacks: Concept-based XAI Dissection—0
Enhancing UAV Security Through Zero Trust Architecture: An Advanced Deep Learning and Explainable AI Analysis—0
Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors—0
The Limits of Perception: Analyzing Inconsistencies in Saliency Maps in XAI—0
How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey—0
A survey on Concept-based Approaches For Model Improvement—0
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making—0
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks—0
Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving—0
Interpretable Machine Learning for Survival AnalysisCode0
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