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AI Audit of Travel Site Reveals Silent Data Rot and a Hidden Rendering Bug

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A travel website operator used an AI agent (Claude Code) to audit the legal cannabis status data for 213 countries and all 50 US states, after recognizing that rapidly changing laws through 2025–2026 had likely made portions of the dataset inaccurate. The audit uncovered three distinct categories of errors: roughly a dozen genuinely outdated entries, around thirty small territories incorrectly bulk-labeled as 'Medical' when many are strict-prohibition jurisdictions, and a silent rendering bug where corrected database entries never appeared on the live site. The rendering failure stemmed from a name-matching mismatch in the site's code — for example, 'Saint Lucia' in the database failing to match 'St Lucia' in the static fallback array — meaning correct data existed but was never displayed to users. The operator flagged that permissive default values, such as labeling unknown jurisdictions as 'Medical,' pose a serious liability risk since they could mislead travelers into carrying substances that are strictly illegal at their destination. The case highlights how data accuracy issues in high-stakes systems can be structural and silent, not just a matter of keeping records up to date.

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AI Audit of Travel Site Reveals Silent Data Rot and a Hidden Rendering Bug · ShortSingh