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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 201–250 of 971 papers

TitleStatusHype
Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions—0
XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Models—0
An Explainable Fast Deep Neural Network for Emotion Recognition—0
Geometric Remove-and-Retrain (GOAR): Coordinate-Invariant eXplainable AI Assessment—0
End-to-end Stroke imaging analysis, using reservoir computing-based effective connectivity, and interpretable Artificial intelligenceCode0
Are Linear Regression Models White Box and Interpretable?—0
XEQ Scale for Evaluating XAI Experience Quality—0
Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review—0
Robustness of Explainable Artificial Intelligence in Industrial Process Modelling—0
Robust and Explainable Framework to Address Data Scarcity in Diagnostic Imaging—0
Impact of satellites streaks for observational astronomy: a study on data captured during one year from Luxembourg Greater RegionCode0
Explainable Image Recognition via Enhanced Slot-attention Based Classifier—0
From Data to Commonsense Reasoning: The Use of Large Language Models for Explainable AI—0
How Reliable and Stable are Explanations of XAI Methods?—0
A Survey of Accessible Explainable Artificial Intelligence Research—0
Explainability of Machine Learning Models under Missing DataCode0
ShapG: new feature importance method based on the Shapley valueCode0
FreqRISE: Explaining time series using frequency maskingCode0
MiSuRe is all you need to explain your image segmentation—0
GFM4MPM: Towards Geospatial Foundation Models for Mineral Prospectivity Mapping—0
GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in ExplanationsCode0
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
Applications of Explainable artificial intelligence in Earth system science—0
CoXQL: A Dataset for Parsing Explanation Requests in Conversational XAI SystemsCode0
Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems—0
Are Large Language Models the New Interface for Data Pipelines?—0
GPU-Accelerated Rule Evaluation and Evolution—0
Transferring Domain Knowledge with (X)AI-Based Learning Systems—0
nn2poly: An R Package for Converting Neural Networks into Interpretable Polynomials—0
Enhancing Counterfactual Image Generation Using Mahalanobis Distance with Distribution Preferences in Feature Space—0
Weak Robust Compatibility Between Learning Algorithms and Counterfactual Explanation Generation Algorithms—0
Probabilities of Causation for Continuous and Vector Variables—0
Unified Explanations in Machine Learning Models: A Perturbation ApproachCode0
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
Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF): A Data-Morphology-based Counterfactual Generation Method for Trustworthy Artificial Intelligence—0
EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods—0
From SHAP Scores to Feature Importance Scores—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
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