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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 701–750 of 971 papers

TitleStatusHype
Explainable Artificial Intelligence (XAI) for Internet of Things: A Survey—0
Predicting and Understanding Human Action Decisions during Skillful Joint-Action via Machine Learning and Explainable-AICode0
Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements—0
Comparing interpretation methods in mental state decoding analyses with deep learning models—0
Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows—0
TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time SeriesCode0
A Psychological Theory of Explainability—0
The Conflict Between Explainable and Accountable Decision-Making Algorithms—0
Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract SettingCode0
A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making—0
Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine LearningCode0
Explainable Anomaly Detection for Industrial Control System CybersecurityCode0
Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamicsCode0
Explainable AI via Learning to Optimize—0
Local Explanation of Dimensionality ReductionCode0
Integrating Prior Knowledge in Post-hoc Explanations—0
Explainable Analysis of Deep Learning Methods for SAR Image Classification—0
Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI EvaluationCode0
Explain yourself! Effects of Explanations in Human-Robot Interaction—0
A Data-Driven Framework for Identifying Investment Opportunities in Private Equity—0
How Deep is Your Art: An Experimental Study on the Limits of Artistic Understanding in a Single-Task, Single-Modality Neural Network—0
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful ModelsCode0
A Meta Survey of Quality Evaluation Criteria in Explanation Methods—0
Concept Embedding Analysis: A Review—0
Explainable Artificial Intelligence for Exhaust Gas Temperature of Turbofan Engines—0
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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