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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

Showing 251275 of 971 papers

TitleStatusHype
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful ModelsCode0
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG SignalsCode0
Explainable Artificial Intelligence for Dependent Features: Additive Effects of CollinearityCode0
Towards explainable artificial intelligence (XAI) for early anticipation of traffic accidentsCode0
An Interaction-based Convolutional Neural Network (ICNN) Towards Better Understanding of COVID-19 X-ray ImagesCode0
Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamicsCode0
Explainable Artificial Intelligence and Multicollinearity : A Mini Review of Current ApproachesCode0
Explainable Anomaly Detection for Industrial Control System CybersecurityCode0
Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature VisualizationCode0
Explainable Artificial Intelligence for Improved Modeling of ProcessesCode0
Explainable Debugger for Black-box Machine Learning ModelsCode0
Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?Code0
Explainability in Music Recommender SystemsCode0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
Do Protein Transformers Have Biological Intelligence?Code0
Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studiesCode0
midr: Learning from Black-Box Models by Maximum Interpretation DecompositionCode0
Evaluating saliency methods on artificial data with different background typesCode0
EAG-RS: A Novel Explainability-guided ROI-Selection Framework for ASD Diagnosis via Inter-regional Relation LearningCode0
Explainability of Machine Learning Models under Missing DataCode0
Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black BoxCode0
Ensemble of Counterfactual ExplainersCode0
Explainable artificial intelligence approaches for brain-computer interfaces: a review and design spaceCode0
Eliminating The Impossible, Whatever Remains Must Be TrueCode0
Ensuring Medical AI Safety: Explainable AI-Driven Detection and Mitigation of Spurious Model Behavior and Associated DataCode0
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