Create a data visualization auditing application that detects charts and reports that could mislead readers, whether intentionally or accidentally.
Allow users to upload CSV files, paste datasets, or configure chart definitions. Examine axis ranges, missing categories, inconsistent time intervals, misleading percentage changes, inappropriate chart types, sample-size limitations, outliers, aggregation choices, and comparisons between incompatible groups.
For every detected issue, display the original visualization, a plain-language explanation, a severity rating, and an alternative visualization that preserves the underlying data more faithfully.
Include a chart comparison mode, statistical sanity checks, data-quality warnings, and a shareable audit report. Allow users to inspect the calculations behind each warning and adjust the detection rules.
Distinguish mathematical errors from presentation risks and legitimate design choices. Do not claim that a chart is deceptive solely because it uses a nonstandard design. Build a clean interface with sample datasets and support exporting corrected charts and findings.
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