mlsanityData to 7 Oct 2026

Explainable Machine Learning for Scientific Insights and Discoveries

arXiv 1905.08883 ↗First posted 21 May 2019cs.LG stat.MLcited by 165

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Abstract (arXiv)

Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scientific insights and discoveries from observational or simulated data. A prerequisite for obtaining a scientific outcome is domain knowledge, which is needed to gain explainability, but also to enhance scientific consistency. In this article we review explainable machine learning in view of applications in the natural sciences and discuss three core elements which we identified as relevant in this context: transparency, interpretability, and explainability. With respect to these core elements, we provide a survey of recent scientific works that incorporate machine learning and the way that explainable machine learning is used in combination with domain knowledge from the application areas.

Key facts. Cited by 165 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 May 2019.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 32; 2022: 41; 2023: 27; 2024: 31; 2025: 23; 2026: 10.

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  1. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 299
  2. 2022On scientific understanding with artificial intelligencecited by 68
  3. 2021Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needscited by 64
  4. 2022Concept Activation Regions: A Generalized Framework For Concept-Based Explanationscited by 57
  5. 2024Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Directioncited by 46
  6. 2022The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanationscited by 41
  7. 2022Going Beyond XAI: A Systematic Survey for Explanation-Guided Learningcited by 35
  8. 2021Toward Explainable AI for Regression Modelscited by 30
  9. 2023Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forwardcited by 28
  10. 2021Towards a Collective Agenda on AI for Earth Science Data Analysiscited by 27
  11. 2022Interpretable Geometric Deep Learning via Learnable Randomness Injectioncited by 27
  12. 2021On Locality of Local Explanation Modelscited by 25
38 more
  1. 2022Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspacescited by 25
  2. 2023An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learningcited by 25
  3. 2021Nonperturbative renormalization for the neural network-QFT correspondencecited by 24
  4. 2024On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMscited by 24
  5. 2022Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomenacited by 21
  6. 2022SoK: Explainable Machine Learning for Computer Security Applicationscited by 20
  7. 2021DySMHO: Data-Driven Discovery of Governing Equations for Dynamical Systems via Moving Horizon Optimizationcited by 19
  8. 2021Explainable Machine Learning with Prior Knowledge: An Overviewcited by 14
  9. 2023Are Data-driven Explanations Robust against Out-of-distribution Data?cited by 14
  10. 2024DEAL: Disentangle and Localize Concept-level Explanations for VLMscited by 14
  11. 2021Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligencecited by 13
  12. 2023REFORMS: Reporting Standards for Machine Learning Based Sciencecited by 13
  13. 2023On the Interpretability of Quantum Neural Networkscited by 13
  14. 2024In-context learning agents are asymmetric belief updaterscited by 13
  15. 2019Mo"ET: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learningcited by 12
  16. 2021Explainable Artificial Intelligence for Human Decision-Support System in Medical Domaincited by 12
  17. 2022A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecastingcited by 12
  18. 2022Explainability in reinforcement learning: perspective and positioncited by 11
  19. 2024Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Surveycited by 11
  20. 2025Interpretable Machine Learning in Physics: A Reviewcited by 11
  21. 2025Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunitiescited by 10
  22. 2021Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programmingcited by 8
  23. 2024Comprehensible Artificial Intelligence on Knowledge Graphs: A surveycited by 8
  24. 2021Physics-informed generative neural network: an application to troposphere temperature predictioncited by 7
  25. 2022MEGAN: Multi-Explanation Graph Attention Networkcited by 7
  26. 2022Renormalization in the neural network-quantum field theory correspondencecited by 7
  27. 2023Interpretable Machine Learning for Discovery: Statistical Challenges & Opportunitiescited by 7
  28. 2022When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challengescited by 6
  29. 2022Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices)cited by 6
  30. 2022Understanding electricity prices beyond the merit order principle using explainable AIcited by 6
  31. 2024What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blockscited by 6
  32. 2024Beyond Accuracy: Ensuring Correct Predictions With Correct Rationalescited by 6
  33. 2025When is a System Discoverable from Data? Discovery Requires Chaoscited by 6
  34. 2022Generalized Permutants and Graph GENEOscited by 5
  35. 2023Non-Uniform Interpolation in Integrated Gradients for Low-Latency Explainable-AIcited by 5
  36. 2023Exploring Effective Priors and Efficient Models for Weakly-Supervised Change Detectioncited by 5
  37. 2023Data-Centric Digital Agriculture: A Perspectivecited by 5
  38. 2024Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challengescited by 5

50 most cited of 165 All 165 (JSON)