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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 576–600 of 971 papers

TitleStatusHype
Foiling Explanations in Deep Neural NetworksCode0
Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research—0
MEGAN: Multi-Explanation Graph Attention NetworkCode1
Crown-CAM: Interpretable Visual Explanations for Tree Crown Detection in Aerial Images—0
Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations—0
Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection—0
Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed InterpretationCode0
SolderNet: Towards Trustworthy Visual Inspection of Solder Joints in Electronics Manufacturing Using Explainable Artificial Intelligence—0
Using explainability to design physics-aware CNNs for solving subsurface inverse problems—0
Explainable Artificial Intelligence in Construction: The Content, Context, Process, Outcome Evaluation Framework—0
Explainable Artificial Intelligence: Precepts, Methods, and Opportunities for Research in Construction—0
REVEL Framework to measure Local Linear Explanations for black-box models: Deep Learning Image Classification case of studyCode0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
Motif-guided Time Series Counterfactual Explanations—0
Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions—0
Backtracking Counterfactuals—0
Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations (XAI)—0
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence—0
A Temporal Type-2 Fuzzy System for Time-dependent Explainable Artificial Intelligence—0
XAI for transparent wind turbine power curve modelsCode1
Sustainable Personalisation and Explainability in Dyadic Data Systems—0
Black Box Model Explanations and the Human Interpretability Expectations -- An Analysis in the Context of Homicide PredictionCode0
Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust PerspectiveCode0
Explaining machine learning models for age classification in human gait analysis—0
Explaining automated gender classification of human gait—0
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