Model Interpretability Publications

Publications in Model Interpretability

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

Model Interpretability

2.5K

Publications

246.8K

Citations

Showing yearly statistics from 2000-2026.

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	Year	Citations
"Why Should I Trust You?" Marco Túlio Ribeiro, Sameer Singh, Carlos Guestrin Artificial IntelligenceEngineeringMachine LearningTrust Management ArchitectureVerification	2016	14K
Explaining and Harnessing Adversarial Examples Ian Goodfellow, Jonathon Shlens, Christian Szegedy arXiv (Cornell University) Artificial IntelligenceData AugmentationMaxout NetworkEngineeringMachine Learning	2014	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
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets Takaya Saito, Marc Rehmsmeier PLoS ONE EngineeringMachine LearningDiagnosisRoc PlotClassification Method	2015	4.2K
Aspects of Scientific Explanation. Michael D. Resnik, Carl G. Hempel Philosophy and Phenomenological Research Scientific ExplanationCognitive ScienceExplanation-based LearningInterpretabilityInductive Reasoning	1966	3.3K
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
Simple means to improve the interpretability of regression coefficients Holger Schielzeth Methods in Ecology and Evolution EngineeringSummary 1Ecological ModellingPredictive AnalyticsEvolutionary Biology	2010	2.9K
Axiomatic Attribution for Deep Networks Mukund Sundararajan, Ankur Taly, Qiqi Yan arXiv (Cornell University) Structured PredictionEngineeringMachine LearningAi FoundationAttribution Methods	2017	2.6K
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar, Peyton Greenside, Anshul Kundaje arXiv (Cornell University) Artificial IntelligenceEngineeringMachine LearningNeural Networks (Machine Learning)Neural Network	2017	2.4K

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