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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 301–325 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
GPU-Accelerated Rule Evaluation and Evolution—0
nn2poly: An R Package for Converting Neural Networks into Interpretable Polynomials—0
Transferring Domain Knowledge with (X)AI-Based Learning Systems—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
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
Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF): A Data-Morphology-based Counterfactual Generation Method for Trustworthy Artificial Intelligence—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
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