Explainable Artificial Intelligence (XAI)

Explainable Artificial Intelligence (XAI) is a research topic within Artificial Intelligence. Science Explorer counts 16k research works in it since 1965. 10.1% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on Explainable Artificial Intelligence (XAI) and the development of interpretable models, visual explanations, and responsible machine learning interpretability. It explores concepts, challenges, and opportunities in XAI, including the use of gradient-based localization, understanding deep neural networks, feature importance, and addressing black box models. The papers also discuss the responsibility and ethical considerations in AI.

• Interpretable Models
• Machine Learning Interpretability
• Visual Explanations
• XAI Concepts
• Model Interpretability
• Gradient-Based Localization
• Deep Neural Networks
• Feature Importance
• Black Box Models
• Responsibility in AI