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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 301–350 of 971 papers

TitleStatusHype
Towards a general framework for improving the performance of classifiers using XAI methods—0
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
Beyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation ModelsCode1
People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AICode0
Explainable Learning with Gaussian ProcessesCode0
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning—0
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
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 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
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 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
Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical SciencesCode0
Linguistically Communicating Uncertainty in Patient-Facing Risk Prediction Models—0
Bridging Human Concepts and Computer Vision for Explainable Face Verification—0
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 Intelligence—0
Unveiling Molecular Moieties through Hierarchical Grad-CAM Graph ExplainabilityCode0
Emergent Explainability: Adding a causal chain to neural network inference—0
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 palsy—0
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
REVEX: A Unified Framework for Removal-Based Explainable Artificial Intelligence in VideoCode0
Word-Level ASR Quality Estimation for Efficient Corpus Sampling and Post-Editing through Analyzing Attentions of a Reference-Free MetricCode1
A comprehensive study on fidelity metrics for XAICode0
Eclectic Rule Extraction for Explainability of Deep Neural Network based Intrusion Detection Systems—0
MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept AlignmentCode1
Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation TestCode1
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
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