arXiv:2508.00937v4 Announce Type: replace-cross Abstract: We present a general approach to visualizing uncertainty in static 2-D statistical graphics. If we treat a visualization as a function of its underlying quantities, uncertainty in those quantities induces a distribution over images. We show how to aggregate these images into a single visualization that represents the uncertainty. The approach can be viewed as a generalization of sample-based approaches that use overlay. Notably, standard representations, such as confidence intervals and bands, emerge with their usual coverage guarantees
The proliferation of complex AI models and data-driven insights necessitates better methods for intuitively understanding underlying uncertainties, a long-standing challenge in statistical visualization.
Improved visualization of uncertainty helps decision-makers trust and appropriately act on data, especially critical in fields like AI where model outputs often lack transparent error margins.
This research provides a more generalized and robust framework for representing uncertainty in static 2-D graphics, potentially leading to more standardized and accurate communication of statistical variability.
- · AI/ML researchers
- · Data scientists
- · UX designers for data products
- · Statistical software developers
- · Providers of misleading or overly confident data visualizations
Wider adoption of uncertainty visualization tools in data analysis and reporting.
Increased user trust in AI-generated insights once their inherent uncertainties are clearly communicated.
Potentially better-informed policy decisions in areas relying heavily on predictive models.
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Read at arXiv cs.LG