A curated collection of papers, datasets, and resources on the topic of misleading visualizations and their interaction with AI research 📊🤖
This field is growing fast, and we’ll keep this repo updated with the latest work 🔥. If you find it useful, don’t forget to leave a star ⭐ to stay in the loop!
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Contact person: Jonathan Tonglet
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- QA with misleading visualizations
- Misleading visualization detection and correction
- Analyses and taxonomies
- Automated fact-checking with visualizations
The deceptive power of misleading visualizations has traditionally been studied through human subject experiments. More recently, researchers have begun testing AI models with similar setups to measure their vulnerability to misleading charts, and to design methods that make them more robust.
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An Empirical Evaluation of the GPT-4 Multimodal Language Model on Visualization Literacy Tasks
Alexander Bendeck, John Stasko. September 2024.
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Xingchen Zeng, Haichuan Lin, Yilin Ye, Wei Zeng. January 2025.
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Protecting multimodal large language models against misleading visualizations
Jonathan Tonglet, Tinne Tuytelaars, Marie-Francine Moens, Iryna Gurevych. February 2025.
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Zixin Chen, Sicheng Song, Kashun Shum, Yanna Lin, Rui Sheng, Huamin Qu. March 2025.
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Benchmarking Visual Language Models on Standardized Visualization Literacy Tests
Saugat Pandey, Alvitta Ottley. March 2025.
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CHARTOM: A Visual Theory-of-Mind Benchmark for LLMs on Misleading Charts
Shubham Bharti, Shiyun Cheng, Jihyun Rho, Jianrui Zhang, Mu Cai, Yong Jae Lee, Martina Rau, Xiaojin Zhu. June 2025.
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The Perils of Chart Deception: How Misleading Visualizations Affect Vision-Language Models
Ridwan Mahbub, Mohammed Saidul Islam, Md Tahmid Rahman Laskar, Mizanur Rahman, Mir Tafseer Nayeem, Enamul Hoque. August 2025.
| Year | Title | Venue | Type | Paper | Dataset |
|---|---|---|---|---|---|
| 2015 | Pandey et al. | CHI | Likert-scale, MCQ | Link | - |
| 2020 | Lauer et O'Brien | SIGDOC | Likert-scale | Link | Link |
| 2023 | CALVI | CHI | MCQ | Link | Link |
| 2025 | CHARTOM | arXiv | MCQ, free-text, rank | Link | Contact authors |
| 2025 | Real-world | arXiv | MCQ | Link | Link |
| 2025 | Misleading ChartQA | EMNLP | MCQ | Link | Link |
| 2025 | Mahbub et al. | VIS | Likert-scale | Link | Link |
Other works introduce datasets and detection techniques aimed at identifying whether a visualization is misleading, and identifying the specific issues it contains.
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Surfacing Visualization Mirages
Andrew McNutt, Gordon Kindlmann, Michael Correll. April 2020.
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VisuaLint: Sketchy In Situ Annotations of Chart Construction Errors
Aspen K. Hopkins, Michael Correll, Arvind Satyanarayan. July 2020.
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VizLinter: A Linter and Fixer Framework for Data Visualization
Qing Chen, Fuling Sun, Xinyue Xu, Zui Chen, Jiazhe Wang, Nan Cao. January 2022.
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Annotating Line Charts for Addressing Deception
Arlen Fan, Yuxin Ma, Michelle Mancenido, Ross Maciejewski. April 2022.
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ChartChecker: A User-Centred Approach to Support the Understanding of Misleading Charts
Tom Biselli, Katrin Hartwig, Niklas Kneissl, Louis Pouliot, Christian Reuter. July 2025.
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Can GPT-4 Models Detect Misleading Visualizations?
Jason Alexander, Priyal Nanda, Kai-Cheng Yang, Ali Sarvghad. October 2024.
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How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?
Leo Yu-Ho Lo, Huamin Qu. January 2025.
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Automated Pipeline for Detecting and Analyzing Misleading Visual Elements
Min Hyeong Kim, Yumin Song, Yungun Kim, Aeri Cho, Soohyun Lee, Hyeon Jeon. April 2025.
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Boosting Data Literacy: The Role of AI in Teaching Detection of Deceptive Charts
Konrad J. Maciborski, Karolina Wysocka, Karolina Żelazowska-Byczkowska, Styliani Kleanthous, Adam Wierzbicki. July 2025.
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Automated Visualization Makeovers with LLMs
Siddharth Gangwar, David A. Selby, Sebastian J. Vollmer. July 2025.
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Amit Kumar Das, Klaus Mueller. August 2025.
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Is this chart lying to me? Automating the detection of misleading visualizations
Jonathan Tonglet, Jan Zimny, Tinne Tuytelaars, Iryna Gurevych. August 2025.
| Year | Title | Venue | Type | Paper | Dataset |
|---|---|---|---|---|---|
| 2024 | Alexander et al. | VIS | Real-world | Link | - |
| 2024 | Lo et al. | TVCG | Real-world | Link | - |
| 2024 | MISCHA-QA | - | Synthetic | - | Link |
| 2025 | DCDM | LNAI | Synthetic | Link | Link |
| 2025 | Misvisfix | VIS | Real-world | Link | Link |
| 2025 | Misviz | arXiv | Real-world | Link | Link |
| 2025 | Misviz-synth | arXiv | Synthetic | Link | Link |
Other works propose methods to correct the code and detection techniques aimed at identifying whether a visualization is misleading, and identifying the specific issues it contains.
The following studies provide taxonomies of misleading visualizations and analyze their impact on web users.
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Misinformed by Visualization: What Do We Learn From Misinformative Visualizations?
Leo Yu-Ho Lo, Ayush Gupta, Kento Shigyo, Aoyu Wu, Enrico Bertini, Huamin Qu. August 2022.
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Misleading Beyond Visual Tricks: How People Actually Lie with Charts
Maxim Lisnic, Cole Polychronis, Alexander Lex, Marina Kogan. April 2023.
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Maxim Lisnic, Alexander Lex, Marina Kogan. May 2024.
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Xingyu Lan, Yu Liu. January 2025.
Some works have explored scenarios where the visualizations are not deceiving. Instead, they are used as reliable evidence to detect false claims with automated fact-checking systems.
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Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking
Mubashara Akhtar, Oana Cocarascu, Elena Simperl. May 2023.
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ChartCheck: Explainable Fact-Checking over Real-World Chart Images
Mubashara Akhtar, Nikesh Subedi, Vivek Gupta, Sahar Tahmasebi, Oana Cocarascu, Elena Simperl. AUgust 2024.
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ClimateViz: A Benchmark for Statistical Reasoning and Fact Verification on Scientific Charts
Ruiran Su, Jiasheng Si, Zhijiang Guo, Janet B. Pierrehumbert. June 2025.
