# In Defence of Visual Analytics Systems: Replies to Critics

arXiv: https://arxiv.org/abs/2201.09772 · HTML: https://mlsanity.com/paper/2201.09772/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2022.

## Built on by (most cited first, 6 of 6)

- [LightVA: Lightweight Visual Analytics with LLM Agent-Based Task Planning and Execution](https://mlsanity.com/paper/2411.05651/) (2024)
- [HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques](https://mlsanity.com/paper/2203.15753/) (2022)
- [SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks](https://mlsanity.com/paper/2305.03039/) (2023)
- [Urbanite: A Dataflow-Based Framework for Human-AI Interactive Alignment in Urban Visual Analytics](https://mlsanity.com/paper/2508.07390/) (2025)
- [VA-Blueprint: Uncovering Building Blocks for Visual Analytics System Design](https://mlsanity.com/paper/2508.07497/) (2025)
- [Walking through Discussions: A Mobile Visual Analytics System for In-Situ Group Discussion Analysis](https://mlsanity.com/paper/2608.08617/) (2026)

## Abstract (arXiv)

The last decade has witnessed many visual analytics (VA) systems that make successful applications to wide-ranging domains like urban analytics and explainable AI. However, their research rigor and contributions have been extensively challenged within the visualization community. We come in defence of VA systems by contributing two interview studies for gathering critics and responses to those criticisms. First, we interview 24 researchers to collect criticisms the review comments on their VA work. Through an iterative coding and refinement process, the interview feedback is summarized into a list of 36 common criticisms. Second, we interview 17 researchers to validate our list and collect their responses, thereby discussing implications for defending and improving the scientific values and rigor of VA systems. We highlight that the presented knowledge is deep, extensive, but also imperfect, provocative, and controversial, and thus recommend reading with an inclusive and critical eye. We hope our work can provide thoughts and foundations for conducting VA research and spark discussions to promote the research field forward more rigorously and vibrantly.