Yes—AI recommendations can change when you change the language of a prompt, the locale named in it, or the geographic context attached to your account. Published studies have found differences in recommendation quality, destination specificity, factual accuracy and implicit assumptions about countries. Those findings are model- and task-specific, so the reliable way to evaluate a service is a controlled comparison: keep the task and constraints fixed, vary language and locale separately, repeat each condition, and score both the recommended items and the evidence supporting them.
What language and locale can change
Language and locale are related, but they are not the same variable. Language is the wording used in the request. Locale includes a named country or region, destination settings, account location, currency, time zone and other geographic signals. A model may also infer a locale when you do not state one. If you switch from English to Spanish, for example, the model could be responding to language competence, to a different cultural interpretation of the request, or to an inferred Spanish-speaking market. A fair test must separate those possibilities.
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Changes can appear in several observable ways:
- The list of recommended items changes, or the same items move to different ranks.
- Recommendations become more or less relevant to the fixed budget, audience or use case.
- Descriptions contain different levels of specificity, such as named places, products or local services.
- Factual accuracy changes, including incorrect names, prices, laws, opening dates or geographic relationships.
- The answer assumes a country, currency, legal norm or cultural preference that you did not state.
- Geographic references appear because of account or device metadata even when the prompt itself is neutral.
A different answer is not automatically a worse answer. Your comparison needs a predefined scoring rule so that you distinguish legitimate localization from unsupported assumptions or factual errors.
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The evidence shows that effects can occur, but it does not establish a universal behavior for every current consumer assistant. The studies below use different models, datasets and tasks.
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| Study and year | Conditions | Reported result | What it does not establish |
|---|---|---|---|
| Multilingual recommender study, 2024 workshop paper | English, Spanish and Turkish prompt templates tested on ML1M, LastFM and Amazon-Beauty | Non-English prompts generally reduced recommendation performance, with the largest reductions reported for Turkish. Multilingual retraining made performance more balanced across the tested languages, with a slight decrease for English prompts. | It does not predict the behavior of another recommender, dataset or general-purpose assistant. |
| LocQA, 2026 ACL Long Papers | 2,156 locale-dependent questions in 12 languages, evaluated on 32 models; the language was the only locale cue | Authors report a global tendency toward US-relevant answers when the question did not name a locale. Within a language shared by several locales, answers tended to favor the larger-population locale. | It does not show that every answer from every model is US-centered or that language alone caused each difference. |
| Bhagat, Vasisht and Pruthi, 2025 | About 100,000 travel requests and 200,000 story generations across five language models | Travel recommendations corresponding to countries categorized as poorer in the study were less unique and contained fewer location references. Stories about those regions more often expressed hardship and sadness than stories about wealthier countries. | The result concerns two generation tasks and the study’s geographic groupings, not all recommendation systems. |
| Universidad de Deusto tourism study | 210 AI-generated responses in Spanish, Basque, English and Russian about Zumaia, Zarautz and Donostia / San Sebastián | Accuracy, comprehensiveness and structure varied by query language and place-name form. Russian responses were more often generic or erroneous; local-language responses could be more detailed but still factually inaccurate. Destination recognition was lower for non-Latin scripts. | The accessible record does not provide a publication date, and the findings concern these destinations and prompts. |
| Col and Chan, 2026 CustomNLP4U workshop | Neutral prompts tested with geographic metadata and placeholder conditions | Geographic-reference leakage rose by up to 793 times above baseline in the reported conditions. For Llama 3.1-8B, one example rose from 0.04% to 31.7%; the abstract also reports 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. | Leakage measures geographic references, not recommendation quality, and the figures are specific to the paper’s setup. |
LocQA describes its design this way: “The questions do not contain indications of the locales they relate to, other than the querying language itself.” That makes it especially useful when you want to test implicit locale inference rather than an explicitly named country.
Define the question before you test
Start with one recommendation task that can be judged objectively. “Recommend something good” is too vague. A useful task specifies the audience, constraints and output format.
Examples of testable tasks
- Choose three weekend destinations for two adults and one child, with a fixed total budget, a maximum travel time and a preference for museums.
- Recommend three laptops for software development, with a stated operating-system requirement, memory minimum, weight limit and budget.
- Select five films for a defined age group and genre, excluding titles already watched.
Write down the success criteria before running any prompts. For a destination task, criteria might include budget compliance, travel-time accuracy, family suitability, verifiable local details and diversity among the three choices. This prevents you from changing the standard after seeing a favored answer.
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Create a single base prompt containing the task, constraints, requested number of items, output format and level of detail. Keep punctuation, list structure and specificity as similar as possible in every language. Have fluent speakers review translations for meaning, politeness, measurement units and culturally specific terms; literal word-for-word translation can introduce a confounding change.
Use a fixed prompt skeleton
Recommend three options for [audience] who need [task]. Constraints: [budget], [time or size limit], [must-have features], and [exclusions]. For each option, provide [the same fields]. State any uncertainty and do not assume a country unless I name one.Replace the bracketed values once, then translate the completed prompt. Do not add a country name in one language merely because it is customary in that language. If you use local units or currency, record that as a deliberate condition rather than treating it as a translation detail.
Separate the three experimental axes
A minimum comparison has three axes. Testing only “English versus Spanish” cannot tell you whether the difference came from language or from an implied country.
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Axis 1: language with locale held constant
Name the same locale in every prompt, then change only the language. For example, ask in English, Spanish and Turkish about the same city, currency and legal jurisdiction. This tests language-related differences while keeping the target geography stable.
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Axis 2: locale with language held constant
Use one language and explicitly change the named locale: for example, the same travel request for Japan, Canada and Brazil. This tests geographic conditioning and local relevance without changing language competence.
Axis 3: locale left unstated
Remove the country or region entirely and ask the same question in each language. This reproduces the type of ambiguity used by LocQA and reveals whether the model treats the querying language as a locale cue.
A useful test matrix therefore includes each language at a fixed locale, each locale in a fixed language, and an ambiguous version. If you also change account location, currency, time zone or browsing settings, record those as separate conditions rather than claiming that the test isolates language.
Record every setting that can affect an answer
Save the exact prompt and output for every run. Record:
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- Date and time, account region, device location, interface language, currency and time zone.
- Whether browsing, retrieval, plugins or other tools were enabled.
- Personalization, memory, conversation history and recommendation settings.
- Sampling controls or temperature, if exposed by the service.
- The number of runs and any errors, refusals or timeouts.
Start a fresh conversation for each condition unless your test is specifically about personalization. Otherwise, an earlier message can disclose the locale or preference you are trying to measure.
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Run repeated trials, not one-off prompts
- Prepare the full prompt matrix before opening the service.
- Run each condition the same number of times. Generative systems can produce different recommendations from identical prompts.
- Keep tool access and sampling controls fixed. If a service does not expose them, note that limitation.
- Capture the complete response, including caveats, citations, links and refusal text.
- Do not edit outputs before scoring. Correct obvious typos in a separate annotation so the original evidence remains intact.
There is no universally correct number of repetitions for this practical test. More runs give a clearer picture of variation, while fewer runs provide weaker evidence. Always report the number used and show the spread rather than presenting one response as the model’s permanent behavior.
Score both the list and the supporting claims
Use a rubric written before you inspect the results. Score each recommendation against the same criteria, then calculate condition-level summaries such as the average score and the range across runs.
Recommended scoring dimensions
- Item overlap: How many recommended items are shared between conditions?
- Rank stability: Do the same items remain near the top?
- Constraint fit: Does each item meet the stated budget, size, audience and feature requirements?
- Relevance: Does the option solve the user’s actual problem rather than a nearby one?
- Specificity: Does the answer name usable places, products, routes or services instead of generic categories?
- Factual accuracy: Can important claims be checked against reliable, current sources?
- Diversity: Are the options meaningfully different, or are they repetitions of one type?
- Unsupported assumptions: Does the answer silently choose a country, currency, law or cultural norm?
For travel, separately count named locations and verifiable details. A response can recommend the same three destinations in every language yet still differ substantially in accuracy or usefulness. Conversely, a changed list may reflect legitimate local knowledge rather than bias.
Check claims at the right level
Verify dates, opening status, legal rules, prices, distances, transit claims and place names independently. Mark a claim as uncertain when the answer provides no way to verify it. Do not treat a confident tone, fluent grammar or a citation-like format as evidence of correctness.
Analyze differences without overclaiming a cause
First describe what changed: “The Turkish condition shared one of three items with English and had two unsupported distance claims.” Only then discuss possible explanations. A black-box comparison cannot by itself distinguish language competence from training-data coverage, retrieval behavior, locale inference, personalization or random sampling.
Look for patterns across repeated trials:
- If the list changes while locale is fixed, language or language-linked retrieval may be involved.
- If the list changes when only the named locale changes, geographic conditioning is a more direct explanation.
- If ambiguous prompts consistently favor one country, the model may be inferring locale from language or metadata.
- If outputs vary widely within one condition, randomness may be larger than the language or locale effect.
- If specificity falls for one region while factual errors rise, treat those as separate quality problems rather than one “bias score.”
Report descriptive results and causal interpretations separately. “These answers differed” is supported by your records; “the model changed because it lacks knowledge of this language” requires additional evidence.
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A worked travel comparison
Suppose the task is: recommend three two-day family destinations under a fixed budget, with museums, short transfers and no rental car. Create these conditions:
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- English, destination explicitly named, with the same currency and budget.
- Spanish, the same destination and constraints.
- Turkish, the same destination and constraints.
- English, a second destination named, with all other wording unchanged.
- English, no destination named.
- Spanish, no destination named.
- Turkish, no destination named.
Compare overlap, rank, budget compliance, named attractions, transfer times and factual support. If the no-destination Spanish and Turkish answers both default to a large Spanish-speaking or Turkish-speaking market, that is evidence of locale inference in this test—not proof that the model always behaves that way. If a locally written answer contains more attraction names but incorrect opening information, record higher specificity and lower accuracy as two separate scores.
Common mistakes and how to fix them
Changing several variables at once
Changing language, currency, country and account location together makes the result uninterpretable. Return to a fixed locale and change one variable per comparison.
Translating unevenly
An added adjective, omitted constraint or different measurement unit can change recommendations. Use fluent review and a side-by-side checklist of every requirement.
Trusting a single response
One output may be an outlier. Repeat conditions and report run-to-run variation.
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Scoring only whether the list changed
A changed list is not automatically bias, and an unchanged list can still contain different factual errors. Score relevance, accuracy, specificity and assumptions.
Ignoring hidden context
Account region, browsing, memory and location metadata can influence geographic references. Record them, disable what you can, and disclose what you cannot control.
Generalizing from one benchmark
The multilingual recommender study, LocQA, tourism experiments and geographic-leakage paper use different tasks and systems. Treat each as evidence that an effect is possible under stated conditions, not as a ranking of every live AI product.
What a credible report should contain
A useful write-up lets another reader reproduce the comparison. Include:
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- The named locale, ambiguous-locale condition and account settings for each run.
- Product, model/version, date and tool settings.
- Number of repetitions per condition.
- Your scoring rubric and any independent fact-checking sources.
- Examples of changed recommendations, rank shifts, factual errors and unsupported assumptions.
- Run-to-run spread and a clear statement of what the test cannot determine.
When publishing percentages or counts, keep their scope attached: identify the model, task, dataset, region grouping and test date. Do not transfer a figure from a research paper to a different service.
The practical conclusion
AI recommendations can change with language, explicit locale and geographic metadata, and published work documents effects in recommendation benchmarks, travel outputs, tourism answers and neutral prompts. The defensible way to check a service is a controlled, repeated comparison that varies language and locale independently, preserves every setting, and evaluates both the recommended items and their factual support. The result should describe the behavior observed under your recorded conditions, not claim a universal property of all AI systems.
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