# Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions

arXiv: https://arxiv.org/abs/2205.01296 · HTML: https://mlsanity.com/paper/2205.01296/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 91 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 15 of 15)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [Model-Agnostic Interpretability of Machine Learning](https://mlsanity.com/paper/1606.05386/) (2016)
- [Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers](https://mlsanity.com/paper/1801.06889/) (2018)
- [Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors](https://mlsanity.com/paper/2103.15949/) (2021)
- [Deep Learning for IoT Big Data and Streaming Analytics: A Survey](https://mlsanity.com/paper/1712.04301/) (2017)
- [A Survey of Visual Analytics Techniques for Machine Learning](https://mlsanity.com/paper/2008.09632/) (2020)
- [The State of the Art in Integrating Machine Learning into Visual Analytics](https://mlsanity.com/paper/1802.07954/) (2018)
- [Survey of explainable machine learning with visual and granular methods beyond quasi-explanations](https://mlsanity.com/paper/2009.10221/) (2020)
- [Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections](https://mlsanity.com/paper/1908.00475/) (2019)
- [Semiotic Aggregation in Deep Learning](https://mlsanity.com/paper/2104.10931/) (2021)
- [Full interpretable machine learning in 2D with inline coordinates](https://mlsanity.com/paper/2106.07568/) (2021)
- [Visual Analytics and Human Involvement in Machine Learning](https://mlsanity.com/paper/2005.06057/) (2020)
- [MultiSegVA: Using Visual Analytics to Segment Biologging Time Series on Multiple Scales](https://mlsanity.com/paper/2009.00548/) (2020)

## Built on by (most cited first, 3 of 3)

- [Interactive Decision Tree Creation and Enhancement with Complete Visualization for Explainable Modeling](https://mlsanity.com/paper/2305.18432/) (2023)
- [Information Plane Analysis Visualization in Deep Learning via Transfer Entropy](https://mlsanity.com/paper/2404.01364/) (2024)
- [Accelerating Convolutional Neural Network Pruning via Spatial Aura Entropy](https://mlsanity.com/paper/2312.04926/) (2023)

## Abstract (arXiv)

This volume is devoted to the emerging field of Integrated Visual Knowledge Discovery that combines advances in Artificial Intelligence/Machine Learning (AI/ML) and Visualization/Visual Analytics. Chapters included are extended versions of the selected AI and Visual Analytics papers and related symposia at the recent International Information Visualization Conferences (IV2019 and IV2020). AI/ML face a long-standing challenge of explaining models to humans. Models explanation is fundamentally human activity, not only an algorithmic one. In this chapter we aim to present challenges and future directions within the field of Visual Analytics, Visual Knowledge Discovery and AI/ML, and to discuss the role of visualization in visual AI/ML. In addition, we describe progress in emerging Full 2D ML, natural language processing, and AI/ML in multidimensional data aided by visual means.