Article Dans Une Revue Proceedings of CHI 2024, ACM Conference on Human Factors in Computing Systems Année : 2024

V-FRAMER: Visualization Framework for Mitigating Reasoning Errors in Public Policy

Résumé

Existing data visualization design guidelines focus primarily onconstructing grammatically-correct visualizations that faithfullyconvey the values and relationships in the underlying data. However,a designer may create a grammatically-correct visualizationthat still leaves audiences susceptible to reasoning misleaders, e.g.by failing to normalize data or using unrepresentative samples. Reasoningmisleaders are especially pernicious when presenting publicpolicy data, where data-driven decisions can affect public health, safety, and economic development. Through textual analysis, aformative evaluation, and iterative design with 19 policy communicators,we construct an actionable visualization design framework,V-FRAMER, that effectively synthesizes ways of mitigating reasoningmisleaders. We discuss important design considerations forframeworks like V-FRAMER, including using concrete examplesto help designers understand reasoning misleaders, and using ahierarchical structure to support example-based accessing. We furtherdescribe V-FRAMER’s congruence with current practice andhow practitioners might integrate the framework into their existingworkflows. Related materials available at: https://osf.io/q3uta/.

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hal-04474456 , version 1 (23-02-2024)

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Lily W Ge, Matthew Easterday, Matthew Kay, Evanthia Dimara, Peter Cheng, et al.. V-FRAMER: Visualization Framework for Mitigating Reasoning Errors in Public Policy. Proceedings of CHI 2024, ACM Conference on Human Factors in Computing Systems, 2024, ⟨10.1145/3613904.3642750⟩. ⟨hal-04474456⟩

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