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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
Explainable Artificial Intelligence and Multicollinearity : A Mini Review of Current ApproachesCode0
Incorporating uncertainty quantification into travel mode choice modeling: a Bayesian neural network (BNN) approach and an uncertainty-guided active survey framework—0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
CoXQL: A Dataset for Parsing Explanation Requests in Conversational XAI SystemsCode0
Applications of Explainable artificial intelligence in Earth system science—0
Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems—0
Are Large Language Models the New Interface for Data Pipelines?—0
nn2poly: An R Package for Converting Neural Networks into Interpretable Polynomials—0
Transferring Domain Knowledge with (X)AI-Based Learning Systems—0
GPU-Accelerated Rule Evaluation and Evolution—0
Weak Robust Compatibility Between Learning Algorithms and Counterfactual Explanation Generation Algorithms—0
Enhancing Counterfactual Image Generation Using Mahalanobis Distance with Distribution Preferences in Feature Space—0
Unified Explanations in Machine Learning Models: A Perturbation ApproachCode0
Probabilities of Causation for Continuous and Vector Variables—0
Watermarking Counterfactual ExplanationsCode0
Locally Testing Model Detections for Semantic Global Concepts—0
Less is More: Discovering Concise Network ExplanationsCode0
A Transformer variant for multi-step forecasting of water level and hydrometeorological sensitivity analysis based on explainable artificial intelligence technology—0
Comparative Analysis of Hyperspectral Image Reconstruction Using Deep Learning for Agricultural and Biological Applications—0
Explaining Expert Search and Team Formation Systems with ExES—0
A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation—0
EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods—0
From SHAP Scores to Feature Importance Scores—0
Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF): A Data-Morphology-based Counterfactual Generation Method for Trustworthy Artificial Intelligence—0
Empowering Prior to Court Legal Analysis: A Transparent and Accessible Dataset for Defensive Statement Classification and Interpretation—0
Tell me more: Intent Fulfilment Framework for Enhancing User Experiences in Conversational XAI—0
Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation—0
Challenges and Opportunities in Text Generation Explainability—0
Evaluating the Explainable AI Method Grad-CAM for Breath Classification on Newborn Time Series Data—0
ExplainableDetector: Exploring Transformer-based Language Modeling Approach for SMS Spam Detection with Explainability Analysis—0
LLMs for XAI: Future Directions for Explaining Explanations—0
Relevant Irrelevance: Generating Alterfactual Explanations for Image ClassifiersCode0
Counterfactual and Semifactual Explanations in Abstract Argumentation: Formal Foundations, Complexity and Computation—0
False Sense of Security in Explainable Artificial Intelligence (XAI)—0
Explainable Interface for Human-Autonomy Teaming: A Survey—0
Isopignistic Canonical Decomposition via Belief Evolution Network—0
Explainable Multi-Label Classification of MBTI Types—0
An Explainable and Conformal AI Model to Detect Temporomandibular Joint Involvement in Children Suffering from Juvenile Idiopathic Arthritis—0
Towards trustable SHAP scores—0
Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle—0
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients—0
Fiper: a Visual-based Explanation Combining Rules and Feature Importance—0
Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments—0
How should AI decisions be explained? Requirements for Explanations from the Perspective of European Law—0
Towards Robust Ferrous Scrap Material Classification with Deep Learning and Conformal Prediction—0
Concept Induction using LLMs: a user experiment for assessment—0
Explainable Artificial Intelligence Techniques for Accurate Fault Detection and Diagnosis: A Review—0
Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI—0
CNN-based explanation ensembling for dataset, representation and explanations evaluation—0
Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression—0
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