AI localization and cultural bias checker
AI models learn from a lopsided slice of the internet, so their default voice is American, English-first, and middle-class-Western. That voice ships quietly: dates as MM/DD, prices in dollars, idioms about baseball, references to the IRS or “your social security number”. This checker scans AI-generated content for those WEIRD assumptions so you can catch them before the copy reaches users in Yerevan, Lagos, or Jakarta.
How it works
The tool runs a library of pattern detectors over your text, grouped by category: date and number formats, currency and units, legal and institutional references, and culturally-specific idioms or holidays. Each match is reported with the offending phrase, the category, why it is a localization risk, and a neutral alternative when one exists. You can optionally name your target locales to get reminders specific to them — for example, flagging imperial units when your audience uses metric. Everything runs in the browser, so nothing is uploaded.
What WEIRD bias actually looks like in AI-generated content
“WEIRD” (Western, Educated, Industrialized, Rich, Democratic) is a term from cross-cultural psychology describing the population that most AI training data represents. In practice, this bias surfaces in specific, predictable ways.
Date and number formats
The US uses MM/DD/YYYY; the rest of the world mostly uses DD/MM/YYYY or YYYY-MM-DD. A date like “03/04/2026” is genuinely ambiguous — 3 April or 4 March, depending on the reader’s region. This is not a minor formatting preference; it has caused real scheduling errors in international communications. The safe alternative is always to write the month as a word: “4 March 2026.”
Number formatting has the same trap in reverse: the US uses commas as thousands separators (“1,000”) while much of Europe uses a period (“1.000”), and vice versa for the decimal separator. “The plan costs $1,500.00” can be read as 1 dollar and 500 cents in some locales.
Currency assumptions
A bare ”$” assumes the reader knows which dollar (US, Canadian, Australian, Singapore, dozens of others) and implicitly assumes the amount is relevant to their economic context. For genuinely global content, either localise the currency and amount, or label it unambiguously (“USD 1,500”).
Legal and institutional references
AI defaults to US legal and institutional frameworks: “the IRS,” “your state,” “filing a 1099,” “your social security number,” “the DMV,” “Chapter 7 bankruptcy.” These concepts either do not exist in other countries or exist under completely different names and structures. Content intended for UK users should reference HMRC, not the IRS; content for a global audience should refer to “your local tax authority” rather than any named institution.
Cultural idioms and seasonal references
Sports metaphors default to American: “touchdowns,” “hitting it out of the park,” “Monday morning quarterbacking.” Seasons are inverted in the southern hemisphere — “spring cleaning” is an autumn activity in Australia. “Holiday season” implies December in a US/Christian cultural context. These references quietly exclude or confuse readers outside the default cultural frame.
Payment and banking assumptions
“Entering a check number,” references to routing numbers and account numbers in the US nine-digit format, assumptions about credit card structures — these are all US-specific. In much of the world, bank transfers use IBAN and BIC/SWIFT, not routing/account combinations.
Tips and notes
- Neutralize before you branch. Often the cheapest fix is wording that works everywhere (“4 March 2026”, “your local tax authority”) rather than maintaining per-locale variants of the same content.
- Label currency unambiguously. “USD 1,500” is clear anywhere; “$1,500” is ambiguous; “1,500 dollars” is slightly better but still assumes one dollar.
- Watch institutional references. Generalise (“your tax authority”) or localise them — never assume a named government body is universal.
- Idioms and seasons travel badly. Sports metaphors, seasonal references, and holiday assumptions rarely survive cultural translation. The checker flags the most common patterns; read the full text with this lens yourself as well.