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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 276–300 of 971 papers

TitleStatusHype
Explainable Activity Recognition for Smart Home Systems—0
CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures—0
An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model—0
Explainability is NOT a Game—0
BSED: Baseline Shapley-Based Explainable Detector—0
A Novel Explainable Artificial Intelligence Model in Image Classification problem—0
AgroXAI: Explainable AI-Driven Crop Recommendation System for Agriculture 4.0—0
Explainability of deep vision-based autonomous driving systems: Review and challenges—0
Explainability in Deep Reinforcement Learning—0
A Grounded Interaction Protocol for Explainable Artificial Intelligence—0
Explainability for identification of vulnerable groups in machine learning models—0
Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications—0
Enhancing UAV Security Through Zero Trust Architecture: An Advanced Deep Learning and Explainable AI Analysis—0
Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution—0
Achieving Explainability for Plant Disease Classification with Disentangled Variational Autoencoders—0
Enhancing Counterfactual Image Generation Using Mahalanobis Distance with Distribution Preferences in Feature Space—0
Ensembles of Convolutional Neural Networks models for pediatric pneumonia diagnosis—0
Breaking Down Financial News Impact: A Novel AI Approach with Geometric Hypergraphs—0
A Novel Approach for Semiconductor Etching Process with Inductive Biases—0
Bridging Human Concepts and Computer Vision for Explainable Face Verification—0
Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing—0
Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios—0
Evaluating quantum generative models via imbalanced data classification benchmarks—0
Multihop: Leveraging Complex Models to Learn Accurate Simple Models—0
Most General Explanations of Tree Ensembles (Extended Version)—0
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