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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 676–700 of 971 papers

TitleStatusHype
Explainable Deep Learning Methods in Medical Image Classification: A SurveyCode1
A Song of (Dis)agreement: Evaluating the Evaluation of Explainable Artificial Intelligence in Natural Language ProcessingCode1
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
Local Explanation of Dimensionality ReductionCode0
Explainable AI via Learning to Optimize—0
Integrating Prior Knowledge in Post-hoc Explanations—0
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraintsCode5
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
Concept Embedding Analysis: A Review—0
A Meta Survey of Quality Evaluation Criteria in Explanation Methods—0
Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models—0
Explainable Artificial Intelligence for Exhaust Gas Temperature of Turbofan Engines—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
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language TasksCode1
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