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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 351–400 of 971 papers

TitleStatusHype
Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational dataCode0
Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives—0
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
Procedural Fairness in Machine LearningCode0
Explainable AI Integrated Feature Engineering for Wildfire Prediction—0
Energy-based Model for Accurate Shapley Value Estimation in Interpretable Deep Learning Predictive ModelingCode0
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
Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors—0
Enhancing UAV Security Through Zero Trust Architecture: An Advanced Deep Learning and Explainable AI Analysis—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
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks—0
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making—0
Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving—0
Towards a general framework for improving the performance of classifiers using XAI methods—0
Interpretable Machine Learning for Survival AnalysisCode0
Explainability through uncertainty: Trustworthy decision-making with neural networks—0
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems—0
XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization—0
A Survey of Explainable Knowledge Tracing—0
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning—0
People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AICode0
Explainable Learning with Gaussian ProcessesCode0
An Explainable AI Framework for Artificial Intelligence of Medical Things—0
Explainable AI for Embedded Systems Design: A Case Study of Static Redundant NVM Memory Write Prediction—0
XAI-Based Detection of Adversarial Attacks on Deepfake DetectorsCode0
AcME-AD: Accelerated Model Explanations for Anomaly Detection—0
Introducing User Feedback-based Counterfactual Explanations (UFCE)Code0
Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity—0
Position: Explain to Question not to Justify—0
MultiFIX: An XAI-friendly feature inducing approach to building models from multimodal data—0
Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive BiasCode0
Implementing local-explainability in Gradient Boosting Trees: Feature Contribution—0
Automated detection of motion artifacts in brain MR images using deep learning and explainable artificial intelligence—0
You can monitor your hydration level using your smartphone camera—0
Beyond explaining: XAI-based Adaptive Learning with SHAP Clustering for Energy Consumption Prediction—0
Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating—0
XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensics—0
Research on Older Adults' Interaction with E-Health Interface Based on Explainable Artificial Intelligence—0
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