# Visual Analytics for Explainable Deep Learning

arXiv: https://arxiv.org/abs/1804.02527 · HTML: https://mlsanity.com/paper/1804.02527/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2018.

## Built on by (most cited first, 30 of 39)

- [Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities](https://mlsanity.com/paper/2111.06420/) (2021)
- [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021)
- [Foundation Models Meet Visualizations: Challenges and Opportunities](https://mlsanity.com/paper/2310.05771/) (2023)
- [How Does Attention Work in Vision Transformers? A Visual Analytics Attempt](https://mlsanity.com/paper/2303.13731/) (2023)
- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI](https://mlsanity.com/paper/2105.12497/) (2021)
- [To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods](https://mlsanity.com/paper/2106.00461/) (2021)
- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [XAI for All: Can Large Language Models Simplify Explainable AI?](https://mlsanity.com/paper/2401.13110/) (2024)
- [A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics](https://mlsanity.com/paper/2307.03195/) (2023)
- [Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain](https://mlsanity.com/paper/2105.02357/) (2021)
- [A Review of ChatGPT Applications in Education, Marketing, Software Engineering, and Healthcare: Benefits, Drawbacks, and Research Directions](https://mlsanity.com/paper/2305.00237/) (2023)
- [Towards Visual Explainable Active Learning for Zero-Shot Classification](https://mlsanity.com/paper/2108.06730/) (2021)
- [TGHop: An Explainable, Efficient and Lightweight Method for Texture Generation](https://mlsanity.com/paper/2107.04020/) (2021)
- [Integrating LLMs for Explainable Fault Diagnosis in Complex Systems](https://mlsanity.com/paper/2402.06695/) (2024)
- [Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles](https://mlsanity.com/paper/2103.10229/) (2021)
- [ShortcutLens: A Visual Analytics Approach for Exploring Shortcuts in Natural Language Understanding Dataset](https://mlsanity.com/paper/2208.08010/) (2022)
- [The FIX Benchmark: Extracting Features Interpretable to eXperts](https://mlsanity.com/paper/2409.13684/) (2024)
- [TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models](https://mlsanity.com/paper/2110.08429/) (2021)
- [Visual Analytics of Neuron Vulnerability to Adversarial Attacks on Convolutional Neural Networks](https://mlsanity.com/paper/2303.02814/) (2023)
- [VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations](https://mlsanity.com/paper/2104.02797/) (2021)
- [Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Construction](https://mlsanity.com/paper/2303.09590/) (2023)
- [RAG Without the Lag: Interactive Debugging for Retrieval-Augmented Generation Pipelines](https://mlsanity.com/paper/2504.13587/) (2025)
- [Model Science: getting serious about verification, explanation and control of AI systems](https://mlsanity.com/paper/2508.20040/) (2025)
- [AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models](https://mlsanity.com/paper/2101.11970/) (2021)
- [Communicating Uncertainty in Machine Learning Explanations: A Visualization Analytics Approach for Predictive Process Monitoring](https://mlsanity.com/paper/2304.05736/) (2023)
- [Visualization for Recommendation Explainability: A Survey and New Perspectives](https://mlsanity.com/paper/2305.11755/) (2023)
- [XAI for time-series classification leveraging image highlight methods](https://mlsanity.com/paper/2311.17110/) (2023)
- [Global explainability in aligned image modalities](https://mlsanity.com/paper/2112.09591/) (2021)
- [Black-box Error Diagnosis in Deep Neural Networks for Computer Vision: a Survey of Tools](https://mlsanity.com/paper/2201.06444/) (2022)

## 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.