Explainable AI Publications

Publications in Explainable AI

Explore the most influential research papers and scholarly works that have shaped Explainable AI. Use the concept filter below to focus on a specific sub-area of research.

Explainable AI

2.8K

Publications

247.6K

Citations

Showing yearly statistics from 2000-2026.

Page 1

	Year	Citations
Explaining and Harnessing Adversarial Examples Ian Goodfellow, Jonathon Shlens, Christian Szegedy arXiv (Cornell University) Artificial IntelligenceData AugmentationMaxout NetworkEngineeringMachine Learning	2014	8.1K
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Information Fusion Artificial IntelligenceCognitive ScienceEngineeringResponsible AiAgent Technology	2019	8.1K
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead Cynthia Rudin Nature Machine Intelligence Artificial IntelligenceEngineeringMachine LearningData ScienceUse Interpretable Models	2019	7.9K
From local explanations to global understanding with explainable AI for trees Scott Lundberg, Gabriel Erion, Hugh Chen, Nature Machine Intelligence Artificial IntelligenceSymbolic LearningEngineeringMachine LearningData Science	2020	7.7K
A Unified Approach to Interpreting Model Predictions Scott Lundberg, Su‐In Lee arXiv (Cornell University) Artificial IntelligenceEngineeringMachine LearningMachine Learning ToolData Science	2017	7.6K
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) Amina Adadi, Mohammed Berrada IEEE Access Artificial IntelligenceEngineeringMachine LearningIntelligent SystemsCommunication	2018	5.4K
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation Sebastian Bach, Alexander Binder, Grégoire Montavon, PLoS ONE Artificial IntelligenceConvolutional Neural NetworkEngineeringMachine LearningClassification Decision	2015	4.4K
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi‐Velez, Been Kim arXiv (Cornell University) Artificial IntelligenceRigorous EvaluationInterpretable Machine LearningRigorous ScienceEngineering	2017	3.1K
Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader, Convolutional Neural NetworkMachine VisionMachine LearningImage AnalysisEngineering	2018	2.8K
Self-explanations: How students study and use examples in learning to solve problems Cognitive Science Artificial IntelligenceEducational PsychologyEducationCognitionCommunication	1989	2.3K
How the machine ‘thinks’: Understanding opacity in machine learning algorithms Jenna Burrell Big Data & Society Artificial IntelligenceEngineeringMachine LearningBusiness IntelligenceInformation Security	2016	2.2K
Eliciting self-explanations improves understanding, Cognitive Science Educational PsychologyMetacognitionEducationCognitionPsycholinguistics	1994	2.1K

Page 1