Ethical Dimensions of Visualization Research
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Abstract (arXiv)
Visualizations have a potentially enormous influence on how data are used to make decisions across all areas of human endeavor. However, it is not clear how this power connects to ethical duties: what obligations do we have when it comes to visualizations and visual analytics systems, beyond our duties as scientists and engineers? Drawing on historical and contemporary examples, I address the moral components of the design and use of visualizations, identify some ongoing areas of visualization research with ethical dilemmas, and propose a set of additional moral obligations that we have as designers, builders, and researchers of visualizations.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Nov 2018.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 2; 2025: 2; 2026: 1.
10 papers.
- 2023VisText: A Benchmark for Semantically Rich Chart Captioningcited by 112
- 2021Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needscited by 64
- 2021Counterfactual Explanations for Machine Learning: Challenges Revisitedcited by 19
- 2025Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Reviewcited by 16
- 2024Can GPT-4 Models Detect Misleading Visualizations?cited by 10
- 2021User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text Analyticscited by 2
- 2022Exploring Effectiveness of Explanations for Appropriate Trust: Lessons from Cognitive Psychologycited by 0
- 2024Behavior Matters: An Alternative Perspective on Promoting Responsible Data Sciencecited by 0
- 2025Survey for Categorising Explainable AI Studies Using Data Analysis Task Frameworkscited by 0
- 2026When Charts Deceive Web Agents: A Paired Benchmark for Misleading Visualization-Grounded Taskscited by 0
- 2026When Charts Deceive Web Agents: A Paired Benchmark for Misleading Visualization-Grounded Tasks9 Oct 2026
- 2025Survey for Categorising Explainable AI Studies Using Data Analysis Task Frameworks14 Jul 2025
- 2025Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review22 Jan 2025
- 2024Behavior Matters: An Alternative Perspective on Promoting Responsible Data Science7 Oct 2024
- 2024Can GPT-4 Models Detect Misleading Visualizations?8 Aug 2024
- 2023VisText: A Benchmark for Semantically Rich Chart Captioning28 Jun 2023
- 2022Exploring Effectiveness of Explanations for Appropriate Trust: Lessons from Cognitive Psychology5 Oct 2022
- 2021Counterfactual Explanations for Machine Learning: Challenges Revisited14 Jun 2021
- 2021Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs24 Jan 2021
- 2021User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text Analytics6 Jan 2021