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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 726–750 of 971 papers

TitleStatusHype
Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models—0
Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanationsCode0
Optimizing Binary Decision Diagrams with MaxSAT for classification—0
Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications—0
An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival—0
Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement—0
Explainable Machine Learning for Predicting Homicide Clearance in the United States—0
Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black BoxCode0
Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization—0
Explainability for identification of vulnerable groups in machine learning models—0
Deep Learning, Natural Language Processing, and Explainable Artificial Intelligence in the Biomedical Domain—0
Deep Learning Reproducibility and Explainable AI (XAI)—0
Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?Code0
XAI in the context of Predictive Process Monitoring: Too much to RevealCode0
TimeREISE: Time-series Randomized Evolving Input Sample Explanation—0
HaT5: Hate Language Identification using Text-to-Text Transfer Transformer—0
Explainable Machine Learning for Breakdown Prediction in High Gradient RF CavitiesCode0
Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscienceCode0
Explainable AI through the Learning of Arguments—0
Causal Explanations and XAI—0
Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods—0
Visualizing the Diversity of Representations Learned by Bayesian Neural Networks—0
An Explainable Artificial Intelligence Framework for Quality-Aware IoE Service Delivery—0
Explainability in Music Recommender SystemsCode0
POTHER: Patch-Voted Deep Learning-Based Chest X-ray Bias Analysis for COVID-19 DetectionCode0
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