The DOI matches its Crossref record; 92 of the 99 references it cites were found in a registry or an index, and 1 are not checked yet.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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.”
From Considering Visualization Example Galleries (2024) · cites this paper as [9]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707
Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective
Haraway, D. (1988) · Feminist Studies · doi:10.2307/3178066 · Cited by 18,364
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
Cook, R. J., Dickens, B. M., & Fathalla, M. F. (2003) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199241323.003.0025 · Cited by 10,825
The Managed Heart: Commercialization of Human Feeling
Tropman, J. E., & Hochschild, A. R. (1984) · Journal of Policy Analysis and Management · doi:10.2307/3324333 · Cited by 8,750
Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective
Haraway, D. (1988) · PhilPapers (PhilPapers Foundation) · Cited by 6,992
Social Media and Fake News in the 2016 Election
Allcott, H., & Gentzkow, M. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.211 · Cited by 6,767
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682
boyd, D., & Crawford, K. (2012) · Information, Communication & Society · doi:10.1080/1369118x.2012.678878 · Cited by 5,479
Lipton, Z. C. (2018) · Queue · doi:10.1145/3236386.3241340 · Cited by 5,427
Henrich, J., Heine, S. J., & Norenzayan, A. (2010) · Nature · doi:10.1038/466029a · Cited by 3,441
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
Doyle, T. (2017) · The Information Society · doi:10.1080/01972243.2017.1354593 · Cited by 3,111
(2016) · Cited by 2,334
THE QUESTION CONCERNING TECHNOLOGY
(2018) · University of Iowa Press eBooks · doi:10.2307/j.ctt20ks1bm.5 · Cited by 1,941
(2009) · Cited by 1,900
Hursthouse, R. (1999) · Cited by 1,678
Narrative Visualization: Telling Stories with Data
Segel, E., & Heer, J. (2010) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2010.179 · Cited by 1,334
(2013) · The MIT Press eBooks · doi:10.7551/mitpress/9302.001.0001 · Cited by 1,235
(2016) · Cited by 1,111
Counterfactual explanations without opening the black box: automated decisions and the GDPR
Wachter, S., Mittelstadt, B., & Russell, C. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3063289 · Cited by 1,061
The (Un)reliability of Saliency Methods
Kindermans, P.-J., Hooker, S., Adebayo, J., Alber, M., Schütt, K. T., Dähne, S., Erhan, D., & Kim, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-28954-6_14 · Cited by 741
ACM code of ethics and professional conduct
Anderson, R. E. (1992) · CACM · doi:10.1145/129875.129885 · Cited by 676
The Gulf War Did Not Take Place.
Pfohl, S., Baudrillard, J., & Patton, P. (1997) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2076743 · Cited by 670
What Makes a Visualization Memorable?
Borkin, M. A., Vo, A. A., Bylinskii, Z., Isola, P., Sunkavalli, S., Oliva, A., & Pfister, H. (2013) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2013.234 · Cited by 667
An Introduction to Critical Cartography
Crampton, J. W., & Krygier, J. B. (2015) · ACME · doi:10.14288/acme.v4i1.723 · Cited by 582
Arendt, H. (2003) · Edinburgh University Press eBooks · doi:10.1515/9781474470230-039 · Cited by 576
Visualization Rhetoric: Framing Effects in Narrative Visualization
Hullman, J. R., & Diakopoulos, N. A. (2011) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2011.255 · Cited by 563
(2016) · neural information processing systems · Cited by 556
Storytelling: The Next Step for Visualization
Kosara, R., & Mackinlay, J. (2013) · Computer · doi:10.1109/mc.2013.36 · Cited by 494
Callahan, S. P., Freire, J., Santos, E., Scheidegger, C., Silva, C. T., & Vo, H. T. (2006) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1142473.1142574 · Cited by 480
Toward measuring visualization insight
North, C. (2006) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2006.70 · Cited by 456
Useful junk?: the effects of visual embellishment on comprehension and memorability of charts
Bateman, S., Mandryk, R. L., Gutwin, C., Genest, A., McDine, D., & Brooks, C. (2010) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1753326.1753716 · Cited by 404
Gitelman, L. (2013) · Cited by 399
Mapping: A Critical Introduction to Cartography and GIS
Wicks, T. E. (2010) · The Photogrammetric Record · doi:10.1111/j.1477-9730.2010.00610.x · Cited by 393
Researchers Misunderstand Confidence Intervals and Standard Error Bars.
Belia, S., Fidler, F., Williams, J., & Cumming, G. (2005) · Psychological Methods · doi:10.1037/1082-989x.10.4.389 · Cited by 390
Beyond Memorability: Visualization Recognition and Recall
Borkin, M. A., Bylinskii, Z., Kim, N. W., Bainbridge, C. M., Yeh, C. S., Borkin, D., Pfister, H., & Oliva, A. (2015) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2467732 · Cited by 385
Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow
Wongsuphasawat, K., Smilkov, D., Wexler, J., Wilson, J., Mané, D., Fritz, D., Krishnan, D., Viégas, F. B., & WATTENBERG, M. P. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2744878 · Cited by 342
Black, E. (2001) · DigitalGeorgetown (Georgetown University Library) · Cited by 341
ON SOME VICES OF VIRTUE ETHICS
Louden, R. B. (1997) · American Philosophical Quarterly · Cited by 330
The work that visualisation conventions do
Kennedy, H., Hill, R. L., Aiello, G., & Allen, W. (2016) · Information Communication & Society · doi:10.1080/1369118x.2016.1153126 · Cited by 306
Williams, B. A. O., & Nagel, T. (1976) · Aristotelian Society Supplementary Volume · doi:10.1093/aristoteliansupp/50.1.115 · Cited by 296
(2007) · Digital Commons at University of Maryland Carey Law (University of Maryland Francis King Carey School of Law) · Cited by 277
(2011) · Choice Reviews Online · doi:10.5860/choice.48-3417 · Cited by 271
Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error
Correll, M., & Gleicher, M. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346298 · Cited by 258
Self-citationvan Wijk, J. J. (2006) · doi:10.1109/vis.2005.102 · Cited by 253
Kay, M., Kola, T., Hullman, J. R., & Munson, S. A. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858558 · Cited by 239
Talbot, J., Lee, B., Kapoor, A., & Tan, D. S. (2009) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1518895 · Cited by 231
Mapping: A Critical Introduction to Cartography and GIS
Crampton, J. W. (2010) · Cited by 228
Vartak, M., Rahman, S., Madden, S. R., Parameswaran, A., & Polyzotis, N. (2015) · Proceedings of the VLDB Endowment · doi:10.14778/2831360.2831371 · Cited by 222
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
Verma, S. (2019) · Vikalpa: The Journal for Decision Makers · doi:10.1177/0256090919853933 · Cited by 219
The Persuasive Power of Data Visualization
Pandey, A. V., Manivannan, A., Nov, O., Satterthwaite, M., & Bertini, E. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346419 · Cited by 217
Automated extraction and visualization of information for technological intelligence and forecasting
Zhu, D., & Porter, A. L. (2002) · Technological Forecasting and Social Change · doi:10.1016/s0040-1625(01)00157-3 · Cited by 196
Henrich, J., Heine, S. J., & Norenzayan, A. (2015) · American Psychological Association eBooks · doi:10.1037/14805-007 · Cited by 194
In Pursuit of Error: A Survey of Uncertainty Visualization Evaluation
Hullman, J., Qiao, X., Correll, M., Kale, A., & Kay, M. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2864889 · Cited by 187
Self-citationDörk, M., Feng, P., Collins, C. M., & Carpendale, S. (2013) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2468356.2468739 · Cited by 172
Assessing the Effect of Visualizations on Bayesian Reasoning through Crowdsourcing
Micallef, L., Dragicevic, P., & Fekete, J. (2012) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.199 · Cited by 166
Against empathy: The case for rational compassion
Shackford, S. R. (2018) · The Journal of Trauma: Injury, Infection, and Critical Care · doi:10.1097/ta.0000000000002071 · Cited by 139
Non-expert interpretations of hurricane forecast uncertainty visualizations
Ruginski, I. T., Boone, A. P., Padilla, L., Liu, L., Heydari, N. D., Kramer, H., Hegarty, M., Thompson, W. B., House, D. H., & Creem-Regehr, S. H. (2016) · Spatial Cognition and Computation · doi:10.1080/13875868.2015.1137577 · Cited by 134
Wall, E., Blaha, L. M., Franklin, L. R., & Endert, A. (2017) · IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast.2017.8585669 · Cited by 129
The Moral Character of Cryptographic WorkPreprint
Rogaway, P. (2016) · IACR Cryptology ePrint Archive · Cited by 127
Frames and Slants in Titles of Visualizations on Controversial Topics
Kong, H., Liu, Z., & Karahalios, K. G. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174012 · Cited by 123
Humanistic Theory and Digital Scholarship
Drucker, J. (2012) · University of Minnesota Press eBooks · doi:10.5749/minnesota/9780816677948.003.0011 · Cited by 122
D’Ignazio, C., & Klein, L. F. (2016) · Cited by 118
Boy, J., Pandey, A. V., Emerson, J., Satterthwaite, M., Nov, O., & Bertini, E. (2017) · Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025512 · Cited by 104
Sketchy Rendering for Information Visualization
Wood, J., Isenberg, P., Tobias Isenberg, A., Dykes, J. A., Boukhelifa, N., & Slingsby, A. (2012) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.262 · Cited by 95
Investigating the Effect of the Multiple Comparisons Problem in Visual Analysis
Zgraggen, E., Zhao, Z., Zeleznik, R. C., & Kraska, T. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174053 · Cited by 94
The Attraction Effect in Information Visualization
Dimara, E., Bezerianos, A., & Dragicevic, P. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2598594 · Cited by 89
Engaging the ethics of data science in practice
Barocas, S., & Boyd, D. (2017) · Communications of the ACM · doi:10.1145/3144172 · Cited by 86 · cited 1×
“Rather, the process of observing the world and quantifying it is a political act, and deserves ethical consideration [6].”
BackgroundExplainers: Expert Explorations with Crafted Projections
Gleicher, M. L. (2013) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2013.157 · Cited by 80
The ACM Code of Ethics and Professional Conduct
Brinkman, B., & Carter, K. (2017) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3017680.3022340 · Cited by 71
Mitigating the Attraction Effect with Visualizations
Dimara, E., Bailly, G., Bezerianos, A., & Franconeri, S. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2865233 · Cited by 67
Design Exposition with Literate Visualization
Wood, J., Kachkaev, A., & Dykes, J. A. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2864836 · Cited by 61
The design and composition of persuasive maps
Muehlenhaus, I. (2013) · Cartography and Geographic Information Science · doi:10.1080/15230406.2013.783450 · Cited by 55
Ashworth, J., Geys, B., & Heyndels, B. (2006) · Public Choice · doi:10.1007/s11127-005-9006-8 · Cited by 54
HindSight: Encouraging Exploration through Direct Encoding of Personal Interaction History
Feng, M., Deng, C., Peck, E. M., & Harrison, L. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2599058 · Cited by 52
Cotter, C. (2010) · doi:10.1017/cbo9780511811975.016 · Cited by 50
Hill, R. L., Kennedy, H., & Gerrard, Y. (2016) · Journal of Communication Inquiry · doi:10.1177/0196859916666041 · Cited by 48
(2021) · University of Pittsburgh Press eBooks · doi:10.2307/j.ctv1ghv4s4.8 · Cited by 46
Adaptive Privacy-Preserving Visualization Using Parallel Coordinates
Dasgupta, A., & Kosara, R. (2011) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2011.163 · Cited by 45
Wang, X., Chen, W., Chou, J., Bryan, C., Guan, H., Chen, W., Pan, R., & Ma, K. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2865021 · Cited by 43
Argument and Authority in the Visual Representations of Science
Richards, A. R. (2003) · Technical Communication Quarterly · doi:10.1207/s15427625tcq1202_3 · Cited by 42
Hecht, B. J., Wilcox, L. G., Bigham, J. P., Schöning, J., Hoque, M. E., Ernst, J. B., Bisk, Y., Russis, L. D., Yarosh, S., Anjum, B., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.09544 · Cited by 37
Boukhelifa, N., & Duke, D. J. (2009) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/1520340.1520616 · Cited by 36
Wacharamanotham, C., Subramanian, K., Völkel, S. T., & Borchers, J. (2015) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702347 · Cited by 35
Professional Services, IBM, and the Holocaust
Dillard, J. (2003) · Journal of Information Systems · doi:10.2308/jis.2003.17.2.1 · Cited by 31
Designing for Uncertainty in HCI
Greis, M., Hullman, J. R., Correll, M., Kay, M., & Shaer, O. (2017) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3027063.3027091 · Cited by 29
Self-citationKosara, R., & Haroz, S. (2018) · IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) · doi:10.1109/beliv.2018.8634392 · Cited by 27
Toward Sustainable Insights, or Why Polygamy is Bad for You.
Binnig, C., Stefani, L. D., Kraska, T., Upfal, E., Zgraggen, E., & Zhao, Z. (2017) · Conference on Innovative Data Systems Research · Cited by 25
Vines, P., Roesner, F., & Kohno, T. (2017) · doi:10.1145/3139550.3139567 · Cited by 19
Skipping the Replication Crisis in Visualization: Threats to Study Validity and How to Address ThemPreprint
Kosara, R., & Haroz, S. (2018) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/f8qey · Cited by 9
Pu, X., & Kay, M. (2018) · doi:10.31219/osf.io/4jbh2 · Cited by 7
Research works printed but not linked: 17
- Hannah Arendt. 2006. Eichmann in jerusalem. Penguin.
- 10.5555/516978 · DOI
- danah boyd. 2016. Undoing the neutrality of big data. Florida Law Review 67 (2016), 226--232.
- Maciej Ceglowski. 2014. The Internet with a Human Face. talk at Beyond Tellerrand in Düsseldorf, Germany, on 20 (2014).
- Michael Correll and Jeffrey Heer. 2017. Black hat visualization. In Workshop on Dealing with Cognitive Biases in Visualisations (DECISIVe), IEEE VIS.
- Craig Dalton and Jim Thatcher. 2014. What does a critical data studies look like, and why do we care? Seven points for a critical approach to ?big data'. Society and Space 29 (2014).
- Catherine D'Ignazio and Lauren Klein. 2019. Data Feminism. MIT Press. 2018 Draft.
- Sam Dragga and Dan Voss. 2001. Cruel pies: The inhumanity of technical illustrations. Technical communication 48, 3 (2001), 265-- 274.
- W.E.B. Du Bois. 1900. Proportion of freemen and slaves among American Negroes. http://hdl.loc.gov/loc.pnp/ppmsca.33913. A series of statistical charts illustrating the condition of the descendants CHI 2019 May 4--9 2019 Glasgow Scotland Uk Michael Correll of former African slaves now in residence in the United States of America.
- W.E.B. Du Bois et al. 1926. Criteria of Negro art. Crisis 32, 6 (1926), 290--297.
- Ryan Gallagher. 2018. Google Shut Out Privacy and Security Teams From Secret China Project. The Intercept (Nov 2018). https: //theintercept.com/2018/11/29/google-china-censored-search/
- Ryan Gallagher. 2018. Senior Google Scientist Resigns Over "Foreiture of our Values" in China. The Intercept (Sep 2018). https://theintercept. com/2018/09/13/google-china-search-engine-employee-resigns/
- Zachary C Lipton. 2016. The mythos of model interpretability. arXiv preprint arXiv:1606.03490 (2016). · arXiv
- 10.5555/3002861 · DOI
- DJ Patil Hilary Mason and Mike Loukides. 2018. Ethics and Data Science. O'Reilly.
- Jack Poulson. 2018. I Quit Google Over Its Censored Chinese Search Engine. The Company Needs to Clarify Its Position on Human Rights. The Intercept (Sep 2018). https://theintercept.com/2018/ 12/01/google-china-censorship-human-rights/
- Sarah Slobin. 2014. What If the Data Visualization is Actually People? Source (Apr 2014). https://source.opennews.org/articles/ what-if-data-visualization-actually-people/
Cited by
Sorted by citations
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Verma, S., Boonsanong, V., Hoang, M., Hines, K., Dickerson, J. P., & Shah, C. S. (2024) · ACM Computing Surveys · doi:10.1145/3677119 · Cited by 723
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Verma, S., Boonsanong, V., Hoang, M., Hines, K. E., Dickerson, J. P., & Shah, C. (2020) · arXiv · doi:10.48550/arxiv.2010.10596 · Cited by 267
Counterfactual Explanations for Machine Learning: A Review.Preprint
Verma, S., Dickerson, J. P., & Hines, K. (2020) · arXiv (Cornell University) · Cited by 223
Lee, C., Yang, T., Inchoco, G. D., Jones, G. M., & Satyanarayan, A. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445211 · Cited by 214
Criteria for Rigor in Visualization Design Study
Meyer, M., & Dykes, J. A. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934539 · Cited by 167
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 ›
Are you an author, or do you see a problem with this page? Request a review
