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Yes—an AI system can recommend different things when you change the language of your prompt, the locale you name, or the location inferred from your account and device. Published studies have found changes in recommendation quality, destination specificity, factual accuracy, and even the geographic assumptions in otherwise neutral answers. Those effects are model-, task-, and dataset-dependent, so one different response does not prove a general language bias.
The reliable way to check is a controlled comparison: keep the task and constraints identical, vary language and locale as separate variables, repeat every condition, and score both the recommended items and the evidence supporting them. The procedure below lets you determine whether a difference is real, how large it is, and whether it is more plausibly caused by language, locale inference, personalization, retrieval, or normal generation randomness.
What “language” and “locale” mean in an AI recommendation test
Language is the language used to write the request and receive the answer. Locale is the geographic and cultural context that can affect what counts as relevant: country, region, city, currency, laws, school calendar, units, spelling conventions, or local availability.
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They are related but not interchangeable. A Spanish prompt might lead a model to assume Spain, Mexico, Argentina, or the United States. An English prompt might be interpreted as US-centric even when the user lives elsewhere. A named destination can hold locale constant while language changes; an unchanged language can reveal what happens when only the named country changes.
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Locale can also be implicit. A service may receive location metadata, account country, browser settings, IP-derived region, or a previous conversation. A comparison that changes those settings at the same time as the prompt language cannot establish a language effect.
What published studies have found
The findings below show why a controlled test is worthwhile. They are not a ranking of current consumer assistants, and they do not predict every model or recommendation task.
| Study and scope | Observed difference | What it does—and does not—show |
|---|---|---|
| 2024 workshop study; English, Spanish and Turkish recommendation prompts on ML1M, LastFM and Amazon-Beauty | Non-English prompts generally reduced recommendation performance, with the largest decline reported for Turkish. Multilingual retraining made performance more balanced across the tested languages, with a slight decrease for English prompts. | Evidence for one recommender, three datasets and those prompt templates; it is not a universal result for every assistant or domain. |
| LocQA, 2,156 questions in 12 languages evaluated on 32 models (ACL Long Papers, 2026) | Locale-dependent questions omitted the target locale, leaving the querying language as the only cue. The authors report a global tendency toward US-relevant answers and, within a shared language, a preference for larger-population locales. | Shows that language can act as an implicit locale signal. It does not prove that every answer in a language is US-oriented. |
| Bhagat, Vasisht and Pruthi (2025); about 100,000 travel requests and 200,000 story generations across five language models | Travel recommendations for poorer countries were less unique and contained fewer location references. Stories about those regions more often expressed hardship and sadness than stories about wealthier countries. | Evidence from two generation tasks and five models, not a property established for all recommendation systems. |
| Universidad de Deusto tourism study; 210 responses in Spanish, Basque, English and Russian about Zumaia, Zarautz and Donostia / San Sebastián | Accuracy, completeness and structure varied by query language and place-name form. Foreign-language answers, especially Russian, were more often generic or erroneous. Local-language answers could be more detailed yet still factually wrong, and non-Latin scripts reduced destination recognition. | Shows language and toponym sensitivity in a narrowly defined tourism task. The accessible record does not state a publication date. |
| Col and Chan (2026), geographic-conditioning experiments | Geographic-reference leakage rose by as much as 793 times above baseline under the study’s conditions. For Llama 3.1-8B, one example rose from 0.04% to 31.7%; reported figures were 21.3% for Qwen3-8B and 8.8% for Claude Sonnet 4.6. Replacing an injected location with “Unknown” still raised leakage by up to 72 times above baseline. | These are leakage measurements, not recommendation-quality scores. They show that contextual location signals can influence geographic references even when the immediate prompt is neutral. |
Build a fair comparison before you prompt
1. Choose a task with checkable success criteria
Make the request concrete enough to judge. For example: “Select three weekend destinations for two adults and one child, within a fixed total budget, reachable without a car, and explain the best month to visit.” Other suitable tasks include choosing a laptop for specified software, finding restaurants that meet dietary constraints, or selecting books in a defined genre.
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2. Write one base prompt with fixed constraints
Keep every substantive requirement identical. Have fluent speakers review translations so that politeness, measurement units, cultural references and implied urgency retain the same meaning rather than becoming accidental prompt changes.
Recommend exactly three weekend destinations for a family of three.Constraints: total trip budget is 1,200 [currency]; travel dates are 12–14 September; no rental car; maximum one flight connection.For each destination, provide estimated transport time, one family-suitable activity, one caveat, and the source or basis for any factual claim. Answer in [target language].Use a currency and date format that you intentionally control. If local conversion is part of the question, state that explicitly; otherwise, an apparently different budget can confound language with purchasing power.
3. Create separate language and locale conditions
At minimum, run these conditions:
- Language-only comparison: keep the named destination or country fixed and ask in two or more languages.
- Locale-only comparison: keep the language fixed and change only the explicitly named country, region or city.
- Ambiguous-locale comparison: remove the locale name while retaining the same task, then see whether the language causes an implicit geographic assumption.
- Context-control comparison: repeat the ambiguous prompt after checking account, device, browser and location settings, so hidden metadata is documented.
For example, compare an English, Spanish and Turkish version that all request recommendations for Japan. Then ask the same English prompt about Japan, Spain and Mexico. Finally, ask each language version without naming a country. This design distinguishes translation effects from locale effects and from inference based on language.
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4. Freeze everything else you can
- Use the same model name and exposed version for every condition.
- Run in the same account type and subscription tier.
- Keep browsing, retrieval, plugins and file uploads either enabled for all runs or disabled for all runs.
- Record device, application or browser, country and language settings, time zone, location permission, and any personalization or memory feature.
- Keep sampling controls such as temperature, top-p, seed and maximum output tokens fixed when the service exposes them.
- Start a fresh conversation for each run unless conversation history is itself the variable under test.
If a service does not expose a model version or sampling control, record that limitation instead of implying that the comparison is perfectly reproducible.
A repeatable test procedure
- Prepare the prompt set. Store the base prompt, every translation, the locale variants and the exact output-format instruction in a plain-text file or spreadsheet. Give each condition an identifier such as EN-JP, ES-JP or EN-AMBIGUOUS.
- Check semantic equivalence. Ask fluent speakers to compare each translation with the base version. Resolve differences in units, honorifics, date interpretation, “nearby,” “affordable,” and other words that can change the recommendation.
- Capture the environment. Before each run, record date and time, model and version, account or region setting, location permissions, browsing status and sampling controls. Save the complete prompt and response, including citations or links supplied by the model.
- Randomize run order. Alternate or randomize conditions where practical. This reduces the chance that a temporary service change, news event or rate limit affects one language consistently.
- Repeat every condition. Use the same number of runs per condition. A practical small test might use five to ten runs per condition; report the actual number. Generative systems can produce different lists from identical prompts.
- Normalize before scoring. Resolve spelling variants, translated place names and aliases to a common item identifier. Keep the original wording as a separate field so that loss of specificity remains visible.
- Score the outputs. Use the metrics below for item overlap, rank, relevance, specificity, factual correctness, diversity and unsupported locale assumptions.
- Inspect examples. Preserve representative prompt pairs and outputs, including failures and near-identical answers. Report the spread across runs, not only the most striking difference.
How to score whether recommendations changed
Item overlap and list changes
Convert each answer to a set of normalized items. For two lists A and B, the Jaccard overlap is the number of shared items divided by the number of items appearing in either list. A score of 1 means the same set; 0 means no shared items. Also record additions, removals and substitutions so a reader can see whether one list changed one item or all three.
For ranked lists, calculate how far each shared item moved and whether the top-ranked choice changed. A list can have high overlap but a materially different recommendation if the most suitable item falls from first to third.
Relevance to fixed criteria
Score every item against the requirements you wrote before testing. A simple rubric can award one point for each satisfied constraint—budget, dates, transport, audience and requested category—and reserve a separate field for “cannot verify.” Do not award credit merely because an answer sounds plausible.
Specificity and usefulness
Count concrete, verifiable details such as named neighborhoods, attractions, transit stations, opening periods or product specifications. The travel study described fewer location references in recommendations for poorer countries; a count of named places can reveal a similar pattern in your own outputs.
Factual accuracy
Verify claims against authoritative destination, government, transport, manufacturer or institutional sources appropriate to the task. Record each claim as correct, incorrect, outdated or unverifiable. A longer answer is not a better answer if it contains more unsupported facts.
Diversity and repetition
When evaluating many users or runs, measure how often the same items recur. Distinguish healthy diversity from irrelevant variation: a different destination is not an improvement if it violates the budget. For story or travel-related tests, also code recurring sentiment or hardship framing rather than treating prose style as recommendation quality.
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Locale assumptions
Mark every unstated assumption about country, currency, law, age threshold, school calendar, units, spelling or cultural norm. LocQA used questions such as “What is the legal drinking age?” and “When does the fiscal year start?” because the correct answer depends on locale. An answer that silently selects one country is a measurable outcome, even when its facts are correct for that country.
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Use a prompt whose wording contains no geographic clue, then compare language versions. For example:
What is the legal drinking age? Give a concise answer and name the jurisdiction you are assuming.When does the fiscal year start? State the jurisdiction and whether exceptions apply.Require the model to name its assumed jurisdiction. This turns a hidden assumption into an observable field. The LocQA benchmark used 2,156 locale-dependent questions in 12 languages across 32 models and deliberately omitted the target locale; its authors report that the querying language was the only locale cue. Your test is not that benchmark, but the same structure helps reveal whether a model treats language as a geographic proxy.
Then repeat the question with an explicit locale. If the answer changes only when the locale is named, the earlier difference was likely an inference effect. If it remains different across languages even with the same named locale, language competence, translation, retrieval coverage or formatting may be contributing.
Interpret differences without overclaiming
“The lists differ” is a descriptive result
State exactly what changed: for example, “The Spanish condition shared two of three destinations with English, while the third run had one additional local attraction.” Do not convert that observation into “the model is biased against Spanish” without evidence about why it changed.
Separate plausible causes
- Language competence: the model may understand one language or spelling convention better.
- Locale inference: the language may trigger an assumed country, as in ambiguous LocQA-style questions.
- Training-data coverage: some languages, destinations or product catalogs may have less representation.
- Retrieval behavior: search indexes, available sources and query rewriting can differ by language or region.
- Personalization and metadata: account, device or location signals can inject geography.
- Randomness: sampling can change an answer even when every setting is identical.
Black-box outputs alone rarely identify one cause. A controlled design narrows the possibilities; it does not reveal internal model mechanisms.
Use uncertainty language
Report the number of runs, the percentage or range of outcomes, and exceptions. “In five runs, the Turkish condition produced fewer unique destinations in four” is stronger than presenting one unusually weak answer as a stable pattern. If the sample is small, call the result an exploratory comparison rather than a population estimate.
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Common test failures and how to fix them
Changing language and locale together
A Spanish prompt about Mexico versus an English prompt about Canada tests two variables. Hold the named place constant for a language test, then hold language constant for a locale test.
Using literal translations that alter the task
Words such as “cheap,” “nearby,” “family,” and “weekend” have different pragmatic meanings. Use bilingual review and document any unavoidable wording difference.
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Comparing one response per condition
One response cannot distinguish a systematic effect from sampling noise. Repeat conditions and report run-to-run spread.
Allowing hidden personalization
Location permissions, account history, previous turns and browsing results can change recommendations. Use fresh sessions where possible and record settings that cannot be disabled.
Scoring only fluency
A polished answer can be geographically wrong. Verify factual claims, inspect named places and check every stated constraint.
Ignoring names written in different scripts
Normalize transliterations and alternate place-name forms before counting overlap, but retain the original script when assessing destination recognition. The Deusto tourism study reported lower recognition for non-Latin scripts, so script handling is itself a useful observation.
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The geographic-conditioning figures—such as a reported rise from 0.04% to 31.7% for one Llama 3.1-8B condition—measure location-reference leakage. They do not show that recommendations became more relevant or accurate. Score those outcomes separately.
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Updating the model mid-test
Cloud services can change silently. If the exposed version changes, stop and mark a new test period rather than combining outputs as though they came from one system.
A practical results sheet
Use one row per run and one column per measurable outcome. This structure keeps evidence auditable:
| Field | What to record |
|---|---|
| Condition ID | Language, explicit locale or ambiguous-locale label |
| Prompt text | Exact characters sent, including system or formatting instructions you control |
| Model and date | Displayed model/version, date and time, account tier |
| Context | Country, time zone, location permission, browser or app, memory and browsing status |
| Output items | Normalized item IDs plus original names and order |
| Constraint score | Points or pass/fail for every predefined requirement |
| Facts checked | Correct, incorrect, outdated or unverifiable for each material claim |
| Specificity | Named places, concrete specifications, citations or other predefined detail count |
| Locale assumptions | Country, currency, law, units, calendar or cultural norm assumed without being asked |
| Notes | Errors, refusal, tool failure, unusual formatting or service interruption |
After collection, publish the prompt pairs, scoring rubric, number of runs and a summary of variation. Include at least one typical output and one failure case for each meaningful difference. Readers should be able to tell what was observed from what you infer about its cause.
When a difference matters to a real user
Not every change is harmful. A request for “local restaurants” should change when the named city changes. A translation may appropriately change examples, spelling or units. The concern is an unexplained change that violates fixed constraints, assumes the wrong jurisdiction, omits relevant local options, or introduces factual errors.
For high-stakes topics—law, health, finance, immigration, safety or eligibility—require an explicit jurisdiction and verify the answer independently. If a model will be used by people speaking multiple languages, test each supported language with the same rubric rather than assuming multilingual capability guarantees equal quality.
For travel and cultural recommendations, check both specificity and representation. Published work has reported fewer location references and less uniqueness for poorer-country destinations, while the Deusto study found that local-language answers could be detailed but inaccurate. A useful evaluation therefore asks two separate questions: “Does the answer mention meaningful, relevant places?” and “Are the details correct?”
Limits of what your test can establish
A prompt comparison describes the tested model, version, settings, task and date. It does not rank all AI products or establish how a service will behave after an update. A small number of runs cannot estimate a stable population effect, and a black-box test cannot prove whether a difference came from language understanding, data coverage, retrieval, personalization or randomness.
Keep the exact artifacts so the test can be repeated after a model or interface change. If you compare a current service, record the product and model/version exposed at the time and rerun the same protocol rather than relying on an older result. Treat published statistics as evidence that effects can occur under specified conditions—not as a guarantee that your particular recommendation task will show the same pattern.
Quick checklist
- Define one concrete recommendation task and a scoring rubric before prompting.
- Keep task, constraints, output format and number of items identical.
- Have fluent speakers validate translations.
- Test language changes with the named locale held constant.
- Test locale changes with language held constant.
- Add an ambiguous-locale condition to expose implicit geographic inference.
- Record model/version, date, account, device, region and tool settings.
- Repeat every condition and report the run count.
- Measure overlap, rank, relevance, specificity, factual accuracy, diversity and unsupported assumptions.
- Normalize alternate names without hiding script or spelling differences.
- Separate observed differences from claims about their cause.
- Preserve prompts, outputs, scores and uncertainty so the comparison can be repeated.
The Bottom Line
To find out whether language or locale changes an AI recommendation, vary one at a time, hold every other setting steady, repeat each condition, and score both the list and its factual support. The result will be valid for the model, task, settings and date you recorded—not automatically for every AI system.
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