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.
An image can be inoffensive and still be wrong. This piece covers what actually goes wrong without localisation, how far better prompting gets you, which tools suit which constraint, and the workflow that catches the errors a prompt cannot.
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. Rest of World's analysis of 3,000 generated images found consistent distortion of non-Western subjects, and researchers studying Chinese food culture judged model output to misrepresent the cuisine entirely, with preparation and plating evoking a different region altogether.
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. Researchers analysing 396 images across 12 countries and three models described this dual standard as visual orientalism — Western nations shown through political and modern symbols, Eastern nations through cultural and exotic ones.
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 and Social Stereotype Index, 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 — research shows multilingual prompting can amplify gender stereotypes, and prompting in a non-English language does not reliably produce culturally aligned output. 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; safest for regulated multi-market campaigns | Full commercial IP indemnification — the clear enterprise differentiator |
| Midjourney | Highest aesthetic control for cultural mood, styling and cinematic scenes | Commercial use on all plans; weak text rendering (~30–40% accuracy) |
| Ideogram | Legible text inside images at ~90–95% accuracy — essential 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. Every generator in this table was built on predominantly Western-centric training data.
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. Research on African contexts makes the point sharply: generators routinely depict African individuals through wildlife, traditional attire or impoverished settings rather than contemporary realities, and the corrective comes from culturally grounded human verification, not a better adjective.
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. Refinement reduced stereotype scores by 53–66% across categories; 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. A defined production step with sign-off, not an informal 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. 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.
Sources and further reading
- "Social stereotypes in AI text-to-image generation", AI and Ethics, Springer (2026) — the Social Stereotype Index rubric, the three-model audit, the 58%/66%/53% reductions, and the specificity-dilution trade-off.
- "Bias and representation in AI generated text-to-image in education: A systematic review", ScienceDirect (2026) — PRISMA review of 31 studies.
- "Cultural Bias in Text-to-Image Models: A Systematic Review" (2026) — bias-type frequencies across 58 studies.
- Rest of World, "Generative AI like Midjourney creates images full of stereotypes" — 3,000 generated images and expert assessment of misrepresented Chinese food culture.
- "Visual Orientalism in the AI Era", arXiv — 396 images across 12 countries and 3 models.
- "AfriStereo: A Culturally Grounded Dataset for Evaluating Stereotypical Bias", arXiv.
- "SoS: Analysis of Surface over Semantics in Multilingual Text-To-Image Generation", arXiv — multilingual prompting amplifying gender stereotypes.
- AI Business Weekly, "Best AI Image Generators 2026" — Firefly's IP indemnification and Ideogram versus Midjourney text accuracy.