Short answer. With a three-part workflow: specify culture at the level of place, period and social context rather than nationality; pick a model suited to the job and its licensing needs; and put a native reviewer between generation and publication. Prompt refinement alone is powerful — a 2026 audit of DALL·E 3, Midjourney 6.1 and Stability AI Core measured a 58% drop in geocultural stereotyping — but it carries a documented trade-off: over-neutralised prompts dilute the very cultural specificity you were trying to represent. Human judgement decides where that line sits.
Key takeaways
- A 2026 audit of DALL·E 3, Midjourney 6.1 and Stability AI Core found structured prompt refinement cut geocultural stereotyping by 58%, occupational stereotyping by 66% and adjectival stereotyping by 53%.
- The same study found refined, "debiased" prompts often produce generic, placeless imagery — reducing stereotypes is not the same as depicting a location correctly.
- A 2026 systematic review of 31 peer-reviewed studies found text-to-image bias is pervasive, consistently centring white, male, Western, thin and non-disabled figures.
- No image generator tested across these studies was free of geocultural bias; the cause is training data, not any single model's design.
- Native-speaker review is the only step in the workflow that reliably catches "silent substitution" — plausible but locally wrong details a global reviewer will miss.
What actually goes wrong without localisation?
Models trained on English-language, Western-centric data reproduce a narrow default — and reach for exotic tropes whenever you ask for anywhere else.
The pattern is well documented. A systematic review of 31 peer-reviewed studies found biased representation was pervasive, with images frequently centring white, male, Western, thin and non-disabled figures while age, body and ability diversity were largely overlooked. A separate arXiv study analysing 396 generated images across 12 countries and three models found the same dual standard at work: Western nations were shown through political and modern symbols, other nations through cultural and exotic ones — a pattern the researchers term visual orientalism, the tendency of a model to render one region as contemporary and another as timeless or exotic by default.
Two failure modes matter commercially.
Flattening. Prompts referencing non-Western regions return wildlife, traditional attire or impoverished settings rather than contemporary reality, while Western prompts return cafes, bakeries and modern urban life. This asymmetry shows up consistently enough across studies that it should be treated as a default behaviour to design around, not an occasional glitch.
Silent substitution. The model produces something plausible but wrong, mixing regional cues that only a local viewer will notice — which is precisely the viewer you are advertising to.
Does better prompting fix it?
It fixes a lot, measurably, but it introduces a trade-off you have to manage deliberately.
The most useful evidence comes from a 2026 study in AI and Ethics, which built a bias-detection rubric called the Social Stereotype Index — a scoring method for how strongly a generated image relies on gendered, cultural or occupational stereotypes — audited DALL·E 3, Midjourney 6.1 and Stability AI Core across 100 queries, then applied structured prompt refinement.
| Stereotype category | Reduction after structured prompt refinement |
|---|---|
| Occupational | −66% |
| Geocultural | −58% |
| Adjectival | −53% |
The catch is important, and the same study named it. Refined prompts often produced more neutral, globally generic imagery: a query for a Bangladeshi person returned cityscapes, social events and corporate offices, frequently showing several people at once to signal diversity. Bias fell, but cultural specificity was diluted. A parallel user study found participants often regarded the stereotypical images as more "expected" — a reminder that audience expectation and accurate representation are not the same thing.
Two implications follow. First, debiasing and localisation are different goals: removing a stereotype is not the same as depicting a place correctly. Second, prompt language itself carries bias — a query phrased entirely in a target language does not reliably produce culturally aligned output on its own. Neither problem is solved inside the prompt box alone.
Which tools should you use for which job?
No generator is culturally neutral, so choose on control, licensing and text handling — then localise with process.
| Tool | Strength for localisation | Licensing note |
|---|---|---|
| Adobe Firefly | Trained on licensed and public-domain content; a common choice for regulated multi-market campaigns | Commercial usage terms built around indemnification for enterprise use |
| Midjourney | Highest aesthetic control for cultural mood, styling and cinematic scenes | Commercial use on all plans; comparatively weak at rendering legible text |
| Ideogram | Notably stronger at legible text inside images — useful for localised signage and packaging | Free tier with commercial rights; paid plans for volume |
| Flux (Black Forest Labs) | Open weights and fine-tuning — the route to training on your own regional reference imagery | API and open variants; verify licence per model version |
| Google Imagen / Gemini | Strong photorealism and complex multi-subject scenes | Commercial rights on paid tiers; check per-product terms |
| GPT Image (ChatGPT) | Best at long, detailed prompts — suits the specific cultural briefs localisation requires | Commercial use rights granted |
| Stable Diffusion | Runs locally; full control and custom LoRAs for specific regional aesthetics | Free and open; you own the pipeline and the review burden |
| Canva / Recraft | Fast in-layout variants and vector output for adapting one asset across markets | Commercial-safe tiers; Recraft outputs native SVG |
Tool choice affects control and legal exposure, not cultural accuracy. None of these models were trained primarily on non-Western imagery, so keeping AI-generated images consistent with a brand's own visual identity still depends on the review step below, whichever generator produces the first draft.
What does the localisation workflow look like?
Brief with specificity, generate with the right tool, then review with someone who lives there.
| Prompt like this | Watch for this |
|---|---|
| Name the city and neighbourhood, not the country | Traditional dress in a modern business scene |
| State the decade and the social setting | Poverty or wildlife cues you never asked for |
| Describe the activity, not the ethnicity | Religious symbols placed incorrectly |
| Specify contemporary context explicitly | Mixed-up regional cuisine, script or architecture |
| Name architecture, dress and objects precisely | Gestures that are offensive locally |
| Generate variants, then select — never accept the first output | Generic "global" imagery that belongs nowhere |
Specificity beats neutrality. "A software team in a Dhaka office, 2026" outperforms "a Bangladeshi person".
The review step is the part most teams skip, and it is the only one that reliably catches silent substitution. A model can render a plausible mosque with the wrong regional architecture, a Diwali scene with Chinese New Year colour conventions, or a "traditional" outfit no one has worn for fifty years — errors invisible to a reviewer in London and immediately obvious to the audience in Lagos, Jakarta or Riyadh. The same body of research that documents flattening in the first place points to the same fix: culturally grounded human verification, not a better adjective, is what catches an error like this before publication, and it is the same discipline behind structured human-in-the-loop review of AI content generally.
That is where a delivery network matters more than a tool subscription. Native-speaker review, culturally grounded reference imagery and locale-specific evaluation data are the human-in-the-loop work Lifewood provides across 50+ languages and dialects — turning generated images from plausibly global into credibly local.
The working sequence:
- Brief by place, period and activity. Replace nationality labels with city, decade, setting and what the people are doing.
- Generate a set, not a single image. Structured refinement reduced stereotype scores by 53–66% across categories in the study above; variation lets you select rather than settle.
- Choose the tool for the constraint. Firefly when indemnification matters, Ideogram when localised text appears in the image, Flux or Stable Diffusion when you need to fine-tune on regional references.
- Route every market asset through a native reviewer, following the same quality-control discipline used to check AI-generated content at scale rather than an informal spot-check.
- Keep a per-market do-not-depict list. Gestures, symbols, dress and settings your local reviewers have already flagged, reused as prompt constraints.
- Watch for over-neutralisation. If the localised set could be anywhere, refinement has gone too far and the culture has been sanded off.
- Disclose synthetic imagery where required, the same obligation covered in how AI content labelling law treats realistic synthetic imagery: realistic AI-generated depictions of people carry disclosure duties under the EU AI Act from 2 August 2026, and the obligation sits with the brand publishing them.
Localising still imagery and localising video share the same underlying discipline — brief by place and period, then verify with a native reviewer — covered in more depth in AI video localization for global markets.