# Human-Data Interaction, Exploration, and Visualization in the AI Era: Challenges and Opportunities

arXiv: https://arxiv.org/abs/2603.05542 · HTML: https://mlsanity.com/paper/2603.05542/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 47 of its 134 references that are in the map.
- First posted on arXiv 4 Mar 2026.

## Builds on (most cited first, 30 of 47)

- [Data-centric Artificial Intelligence: A Survey](https://mlsanity.com/paper/2303.10158/) (2023)
- [Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers](https://mlsanity.com/paper/1801.06889/) (2018)
- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [Scaling Up Graph Neural Networks Via Graph Coarsening](https://mlsanity.com/paper/2106.05150/) (2021)
- [A Survey of Visual Analytics Techniques for Machine Learning](https://mlsanity.com/paper/2008.09632/) (2020)
- [VisEval: A Benchmark for Data Visualization in the Era of Large Language Models](https://mlsanity.com/paper/2407.00981/) (2024)
- [Foundation Models Meet Visualizations: Challenges and Opportunities](https://mlsanity.com/paper/2310.05771/) (2023)
- [A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization](https://mlsanity.com/paper/2012.00467/) (2020)
- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [Generative AI for Visualization: State of the Art and Future Directions](https://mlsanity.com/paper/2404.18144/) (2024)
- [Mitigating Hallucination in Multimodal Large Language Model via Hallucination-targeted Direct Preference Optimization](https://mlsanity.com/paper/2411.10436/) (2024)
- [A Deep Generative Model for Graph Layout](https://mlsanity.com/paper/1904.12225/) (2019)
- [Complaint-driven Training Data Debugging for Query 2.0](https://mlsanity.com/paper/2004.05722/) (2020)
- [Auctus: A Dataset Search Engine for Data Augmentation](https://mlsanity.com/paper/2102.05716/) (2021)
- [DeepDrawing: A Deep Learning Approach to Graph Drawing](https://mlsanity.com/paper/1907.11040/) (2019)
- [Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration](https://mlsanity.com/paper/2309.15723/) (2023)
- [Angler: Helping Machine Translation Practitioners Prioritize Model Improvements](https://mlsanity.com/paper/2304.05967/) (2023)
- [DeepGD: A Deep Learning Framework for Graph Drawing Using GNN](https://mlsanity.com/paper/2106.15347/) (2021)
- [PI2: End-to-end Interactive Visualization Interface Generation from Queries](https://mlsanity.com/paper/2107.08203/) (2021)
- [EQUI-VOCAL: Synthesizing Queries for Compositional Video Events from Limited User Interactions [Technical Report]](https://mlsanity.com/paper/2301.00929/) (2023)
- [Graph Neural Networks for Graph Drawing](https://mlsanity.com/paper/2109.10061/) (2021)
- [Smoke: Fine-grained Lineage at Interactive Speed](https://mlsanity.com/paper/1801.07237/) (2018)
- [Data Changes Everything: Challenges and Opportunities in Data Visualization Design Handoff](https://mlsanity.com/paper/1908.00192/) (2019)
- [SmartGD: A GAN-Based Graph Drawing Framework for Diverse Aesthetic Goals](https://mlsanity.com/paper/2206.06434/) (2022)
- [Visualization-Aware Sampling for Very Large Databases](https://mlsanity.com/paper/1510.03921/) (2015)
- [Scalability in Visualization](https://mlsanity.com/paper/2210.06562/) (2022)
- [Rapid Sampling for Visualizations with Ordering Guarantees](https://mlsanity.com/paper/1412.3040/) (2014)
- [Dead or Alive: Continuous Data Profiling for Interactive Data Science](https://mlsanity.com/paper/2308.03964/) (2023)
- [IDEBench: A Benchmark for Interactive Data Exploration](https://mlsanity.com/paper/1804.02593/) (2018)
- [Big Data Visualization Tools](https://mlsanity.com/paper/1801.08336/) (2018)

## Abstract (arXiv)

The rapid advancement of AI is transforming human-centered systems, with profound implications for human-AI interaction, human-data interaction, and visual analytics. In the AI era, data analysis increasingly involves large-scale, heterogeneous, and multimodal data that is predominantly unstructured, as well as foundation models such as LLMs and VLMs, which introduce additional uncertainty into analytical processes. These shifts expose persistent challenges for human-data interactive systems, including perceptually misaligned latency, scalability constraints, limitations of existing interaction and exploration paradigms, and growing uncertainty regarding the reliability and interpretability of AI-generated insights. Responding to these challenges requires moving beyond conventional efficiency and scalability metrics, redefining the roles of humans and machines in analytical workflows, and incorporating cognitive, perceptual, and design principles into every level of the human-data interaction stack. This paper investigates the challenges introduced by recent advances in AI and examines how these developments are reshaping the ways users engage with data, while outlining limitations and open research directions for building human-centered AI systems for interactive data analysis in the AI era.