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Ethical Dimensions of Visualization Research
Open accessAbstract
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.
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“Correll [9], referring to example data more generally, calls for selecting datasets that are of social import or impact as means for advocating for positive change.”
BackgroundCritical Interactivity: Exploration and Narration in Data Visualization
Morini, F., Garretón, M., Pomerance, J., Zeissig, N., de Günther, S., Thomet, F., Freyberg, L., Kyriazis, I., Scholz, A., & Dörk, M. (2025) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2025.3544684 · Cited by 5
Dı́az, P., Onorati, T., & Aedo, I. (2024) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon60312.2024.10578610 · Cited by 5
Nudging with Narrative Visualization: Communicating to a Young Adult Audience in the Pandemic
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Vancisin, T., Orr, M., & Hinrichs, U. (2020) · IEEE Workshop on Visualization for the Digital Humanities (VIS4DH) · doi:10.1109/vis4dh51463.2020.00011 · Cited by 5
Visualization for VillainyPreprint
McNutt, A. M., Huang, L., & Koenig, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.06007 · Cited by 8
Communicating qualitative uncertainty in data visualization
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ChartChecker: A User-Centred Approach to Support the Understanding of Misleading Charts
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Dhawka, P., Lutz, N., & Starbird, K. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757574 · Cited by 5
Elli, T., Bradley, A., Hinrichs, U., & Collins, C. (2022) · Proceedings of DRS · doi:10.21606/drs.2022.447 · Cited by 4
Cybersecurity Data Visualization: Designing a Course for Future High School Students
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Disclose to Tell: a Data Design Framework for Alternative Narratives
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Knoche, H. O., Abdul‐Rahman, A., Clark, L., Ćurčin, V., Huo, Z., Iwaya, L. H., Lemon, O., Mikulík, R., Neate, T., Roper, A., et al. (2023) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3544549.3573798 · Cited by 3
Beyond the p-value: How descriptive statistics unlock insights in veterinary research?
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Metadata
- Authors
- Correll, M.
- Affiliations
- Tableau Software (United States) (US)
- Corresponding author
- Correll, Michael
- ORCID
- Correll, Michael
- Venue
- Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2019-05-02
- Pages
- 1-13
- OpenAlex
- W2952342347
- Semantic Scholar
- 69b2495fe5093ae56a1ccbf9a9e55b87d677fec9
- Cited by
- 150
- References
- 99
- Citations by year
- 2026: 13 · 2025: 26 · 2024: 29 · 2023: 20 · 2022: 24 · 2021: 16 · 2020: 18 · 2019: 4
- FWCI
- 5.00
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Data Visualization and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Ethics in Business and Education
- Keywords
- visual analytics, moral obligation, data visualization design, ethical dilemmas, decision-making, professional ethics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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