Using AI in a Design Portfolio
Type “a successful professional” or “a beautiful woman” into an AI image generator, and look closely at what comes back. For Indian users, it is often startling how little of it looks like the country they actually live in. This is not a hypothetical concern raised by a handful of critics. It has been documented repeatedly by researchers, journalists, and designers who have run the same experiment across thousands of prompts: AI systems trained overwhelmingly on internet data skewed toward the West default to a narrow, often inaccurate version of Indian identity whenever a prompt does not spell out otherwise.
For design students, this matters far beyond curiosity. You will spend a career designing personas, mood boards, marketing visuals, and interfaces meant to serve real Indian users. If the tools you reach for by default quietly reproduce bias, you can end up shipping that bias into client work without ever intending to. This piece breaks down where the bias actually comes from, what it looks like in practice, and what to do about it as a working designer.
Where This Bias Actually Comes From
It helps to start with the mechanism, because it changes how you respond to the problem. AI image and language models learn patterns from enormous datasets scraped largely from the internet, and the internet is not a neutral mirror of the world. English-language, Western-authored content is dramatically overrepresented relative to its share of the global population, and within that content, certain aesthetics, professions, and body types appear far more often tagged as “beautiful,” “successful,” or “professional” than others.
When a model is trained on that imbalance, it does not need any deliberate intention to discriminate in order to produce biased output. If the vast majority of images labelled “CEO” or “doctor” in training data feature light-skinned, Western-dressed subjects, a model asked to generate “an Indian CEO” will often default toward the same visual template, only lightly adjusted, rather than genuinely representing what Indian executives actually look like across the country’s regional and economic diversity.
This is worth naming clearly because it reframes the fix. The problem is not that any single AI company deliberately programmed prejudice into a model. The problem is a data imbalance that gets inherited silently, which means it will keep showing up across new tools and new model versions unless designers actively correct for it in how they prompt, select, and edit AI output.
Skin Tone and Colorism in AI-Generated Images
Skin tone bias is the most visible and best-documented pattern. Independent analyses of AI-generated images of Indian subjects have repeatedly found a consistent skew toward lighter skin tones, even when a prompt explicitly specifies Indian people, and even when the described context (occupation, class, region) has no logical connection to skin tone at all.
More troubling still, some analyses examining how generative models depict different castes and economic classes within Indian prompts have found the models reproducing real-world colorism rather than correcting for it: subjects described in lower-income or lower-caste contexts rendered with noticeably darker skin and rougher visual framing than subjects described as wealthy or professional, even when nothing in the prompt specified skin tone either way. In other words, the bias does not just under-represent darker skin tones on average. In some cases it actively links darker skin to lower status, mirroring a prejudice that Indian society has spent decades trying to work against.
For a design student, the practical risk is straightforward: if you generate a set of “user persona” images for a project using default prompts, you may unknowingly produce a visual set that is lighter-skinned and more upper-class-coded than the actual user base your project claims to serve, simply because that is what the tool defaults to when you do not correct it.
Flattened Cultural Representation — When “India” Becomes a Stereotype
Skin tone is only one part of the picture. Researchers examining AI-generated depictions of Indian life have found the same handful of visual shorthand repeated constantly: generic saris and turbans regardless of region or occasion, temple imagery standing in for the entire country’s spiritual life, and festival scenes that look more like a stock-photo idea of “colourful India” than any specific, real celebration.
This flattening happens in two directions at once. Regionally, it tends to default toward a generic, often northern or urban-metro visual idea of India, underrepresenting the genuinely enormous differences between how life looks in rural Bihar, coastal Kerala, or a mid-sized Gujarati town. Religiously, because a large share of readily available training imagery skews toward one dominant tradition, prompts about “Indian” life or celebration often overrepresent that tradition’s symbols while underrepresenting Islamic, Sikh, Christian, and other communities that are very much part of the country’s actual cultural fabric.
For a Communication Design or Product Design student building a campaign, an app, or packaging meant for a genuinely Indian audience, uncritically accepting this kind of AI-generated imagery risks producing work that feels generic or even alienating to the very users it is meant to represent, despite technically being labelled “Indian.”
Body Types, Ability and the “Default Body” Problem
The same training-data imbalance shows up in body representation. Left to their defaults, most image generators produce slim, young, able-bodied figures far more often than the actual range of ages, body types, and physical abilities present in any real Indian population, urban or rural.
This matters directly and practically for Fashion Design students working on fit visualization, since a garment that looks resolved on an AI-generated “default body” can look completely different, and fit very differently, on the range of real body types a collection actually needs to serve. It matters just as much for Interior and Product Design students thinking about accessibility and ergonomics: a kitchen counter height, a doorway width, or a chair depth generated by an AI tool trained mostly on “default” bodies will quietly under-serve older users, larger users, and users with mobility differences, none of whom are a small or unusual part of the population you are actually designing for.
Language and Voice — Why AI Text Defaults to a Certain English
Bias in AI is not limited to images. Language and copywriting tools trained predominantly on American and British English content tend to default to that register even when writing for a clearly Indian audience, missing the specific rhythms of Indian English and the code-switching between English and regional languages that often reads as far more authentic and trustworthy to local audiences.
For Communication Design students doing brand voice or copywriting work, this is a genuinely practical concern rather than an academic one. A tagline or app microcopy that an AI tool generates in polished American English may need real, deliberate editing to sound like it was written for, and by, someone who actually understands how people in Ahmedabad, Chennai, or Guwahati actually speak and read. Treating AI-generated copy as a finished draft rather than a first pass is one of the simplest ways to avoid shipping work that feels subtly foreign to its intended audience.
What This Means for You as a Design Student
None of this is a reason to avoid AI tools. It is a reason to use them the way you would use any reference material with a known bias: critically, and with a habit of double-checking against reality.
In practice, that means a few concrete habits worth building early. First, always sense-check AI-generated visuals or personas against real reference photography of actual Indian people, homes, and contexts, rather than accepting the first output as representative. Second, treat AI-generated mood boards and personas as a rough first draft, never a final deliverable, the same way you would treat a stock photo search. Third, deliberately prompt for specificity, naming a region, an age range, a skin tone, an economic context, rather than accepting whatever a generic prompt defaults to. Fourth, and most importantly, build your own reference libraries from real photography, fieldwork, and community input wherever a project genuinely matters, rather than relying on AI-generated stand-ins for the people you are designing for.
Recognising this bias is not a side lesson tacked onto design education. It is a core part of what human-centred design has always asked designers to do: check your assumptions against real people, not against a convenient default.
Frequently Asked Questions
No. It means using AI outputs as a starting point to be checked and corrected, not as a finished, trustworthy representation of real people or contexts by default.
Tools and model versions vary and change frequently, so it is more reliable to build a habit of checking any tool’s output against real references than to memorise a single “best” tool, since rankings shift as models are updated.
Compare the output against real photography of the specific group, region, or context you are designing for, and ask whether the result would look accurate to someone who actually belongs to that group.
Some improvement is happening as awareness grows, but the underlying data imbalance is a slow, structural issue, so it is safer to assume the bias is still present than to assume it has been fixed in any given tool.
It affects both. AI-generated personas, user research summaries, and even suggested colour or layout patterns can carry the same defaults, so the same critical checking applies well beyond visual imagery alone.
Conclusion
AI bias in design is not an abstract ethics debate reserved for researchers. It is a practical, everyday consideration for anyone who will use these tools professionally, which today means almost every design student. The goal is not to distrust AI tools entirely, but to understand exactly where their defaults come from, so you can recognise when they are quietly steering your work away from the real people you are meant to be designing for.
This kind of critical, culturally grounded thinking is exactly what design education needs to build alongside technical AI fluency. It is a thread that runs through our Indic Design and Sustainability Studies coursework at Indus Design School, where students are pushed to question defaults rather than simply accept them.
