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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 601–625 of 971 papers

TitleStatusHype
Towards Robust Ferrous Scrap Material Classification with Deep Learning and Conformal Prediction—0
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features—0
Towards the Linear Algebra Based Taxonomy of XAI Explanations—0
Towards the Visualization of Aggregated Class Activation Maps to Analyse the Global Contribution of Class Features—0
Towards Transparent AI: A Survey on Explainable Large Language Models—0
Towards Transparent and Accurate Diabetes Prediction Using Machine Learning and Explainable Artificial Intelligence—0
Towards Understanding Human Functional Brain Development with Explainable Artificial Intelligence: Challenges and Perspectives—0
Toward the application of XAI methods in EEG-based systems—0
Toward the Explainability of Protein Language Models for Sequence Design—0
Transcending XAI Algorithm Boundaries through End-User-Inspired Design—0
Transferability and explainability of deep learning emulators for regional climate model projections: Perspectives for future applications—0
Transfer Learning and Explainable AI for Brain Tumor Classification: A Study Using MRI Data from Bangladesh—0
Transferring Domain Knowledge with (X)AI-Based Learning Systems—0
Leveraging Expert Input for Robust and Explainable AI-Assisted Lung Cancer Detection in Chest X-rays—0
Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy—0
Trustworthy XAI and Application—0
Tuning for Trustworthiness -- Balancing Performance and Explanation Consistency in Neural Network Optimization—0
Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond—0
Uncertainty Quantification of Wind Gust Predictions in the Northeast US: An Evidential Neural Network and Explainable Artificial Intelligence Approach—0
Vehicle Fuel Optimization Under Real-World Driving Conditions: An Explainable Artificial Intelligence Approach—0
A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation—0
Unraveling Pedestrian Fatality Patterns: A Comparative Study with Explainable AI—0
Unraveling the Dilemma of AI Errors: Exploring the Effectiveness of Human and Machine Explanations for Large Language Models—0
(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Comprehensibility—0
Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence—0
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