mlsanityData to 7 Oct 2026

Visual Analytics for Explainable Deep Learning

arXiv 1804.02527 ↗First posted 7 Apr 2018cs.HC cs.LG stat.MLcited by 39

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Abstract (arXiv)

Recently, deep learning has been advancing the state of the art in artificial intelligence to a new level, and humans rely on artificial intelligence techniques more than ever. However, even with such unprecedented advancements, the lack of explanation regarding the decisions made by deep learning models and absence of control over their internal processes act as major drawbacks in critical decision-making processes, such as precision medicine and law enforcement. In response, efforts are being made to make deep learning interpretable and controllable by humans. In this paper, we review visual analytics, information visualization, and machine learning perspectives relevant to this aim, and discuss potential challenges and future research directions.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Apr 2018.

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Citing papers per year: 2021: 12; 2022: 3; 2023: 14; 2024: 4; 2025: 3; 2026: 3.

39 papers.

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  1. 2021Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunitiescited by 125
  2. 2021Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safetycited by 35
  3. 2023Foundation Models Meet Visualizations: Challenges and Opportunitiescited by 31
  4. 2022The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizationscited by 29
  5. 2023How Does Attention Work in Vision Transformers? A Visual Analytics Attemptcited by 29
  6. 2021Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AIcited by 26
  7. 2021To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methodscited by 26
  8. 2023Visual Analytics For Machine Learning: A Data Perspective Surveycited by 25
  9. 2024XAI for All: Can Large Language Models Simplify Explainable AI?cited by 21
  10. 2023A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analyticscited by 17
  11. 2021Explainable Artificial Intelligence for Human Decision-Support System in Medical Domaincited by 12
  12. 2023A Review of ChatGPT Applications in Education, Marketing, Software Engineering, and Healthcare: Benefits, Drawbacks, and Research Directionscited by 12
27 more
  1. 2021Towards Visual Explainable Active Learning for Zero-Shot Classificationcited by 10
  2. 2021TGHop: An Explainable, Efficient and Lightweight Method for Texture Generationcited by 8
  3. 2021Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profilescited by 6
  4. 2022ShortcutLens: A Visual Analytics Approach for Exploring Shortcuts in Natural Language Understanding Datasetcited by 6
  5. 2024Integrating LLMs for Explainable Fault Diagnosis in Complex Systemscited by 6
  6. 2024The FIX Benchmark: Extracting Features Interpretable to eXpertscited by 5
  7. 2021TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Modelscited by 4
  8. 2023Visual Analytics of Neuron Vulnerability to Adversarial Attacks on Convolutional Neural Networkscited by 4
  9. 2021VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representationscited by 3
  10. 2023Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Constructioncited by 3
  11. 2025RAG Without the Lag: Interactive Debugging for Retrieval-Augmented Generation Pipelinescited by 3
  12. 2025Model Science: getting serious about verification, explanation and control of AI systemscited by 3
  13. 2021AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Modelscited by 2
  14. 2023Communicating Uncertainty in Machine Learning Explanations: A Visualization Analytics Approach for Predictive Process Monitoringcited by 2
  15. 2023Visualization for Recommendation Explainability: A Survey and New Perspectivescited by 2
  16. 2023XAI for time-series classification leveraging image highlight methodscited by 2
  17. 2021Global explainability in aligned image modalitiescited by 1
  18. 2022Black-box Error Diagnosis in Deep Neural Networks for Computer Vision: a Survey of Toolscited by 1
  19. 2025A Rigorous Behavior Assessment of CNNs Using a Data-Domain Sampling Regimecited by 1
  20. 2026KEditVis: A Visual Analytics System for Knowledge Editing of Large Language Modelscited by 1
  21. 2023Explainable Machine Learning: The Importance of a System-Centric Perspectivecited by 0
  22. 2023Evaluating how interactive visualizations can assist in finding samples where and how computer vision models make mistakescited by 0
  23. 2023Detection of Non-uniformity in Parameters for Magnetic Domain Pattern Generation by Machine Learningcited by 0
  24. 2023A Comparative Visual Analytics Framework for Evaluating Evolutionary Processes in Multi-objective Optimizationcited by 0
  25. 2024An Explainable Fast Deep Neural Network for Emotion Recognitioncited by 0
  26. 2026Visualization of Machine Learning Models through Their Spatial and Temporal Listenerscited by 0
  27. 2026Spatial Action Review: A Visual Analytics Dashboard for Auditing Language-to-Action Hand-offs in Electron Microscopycited by 0