Whose English Is Your AI Speaking?

TL;DR — What You Need To Know

• Most AI tools are trained on mainstream American English, ignoring global Englishes like Singlish or Indian English
• This leads to bias, miscommunication, and exclusion in real-world applications
• To fix it, we need AI that recognises linguistic diversity—not corrects it.

English Bias In AI

Here’s a fun fact that’s not so fun when you think about it: 90% of generative AI training data is in English. But not just any English. Not Nigerian English. Not Indian English. Not the English you’d hear in Singapore’s hawker centres or on the streets of Liverpool. Nope. It’s mostly good ol’ mainstream American English.

That’s the voice most AI systems have learned to mimic, model, and prioritise. Not because it’s better. But because that’s what’s been fed into the system.

A Monolingual Machine in a Multilingual World

Let’s be clear: English isn’t one language. It’s many. About 1.5 billion people speak it, and almost all of them do so with their own twist. Grammar, vocabulary, intonation, slang—it all varies.

But when your AI tools—from autocorrect to resume scanners—are only trained on one flavour of English (mostly US-centric, polished, white-collar English), a lot of other voices start to disappear. And not quietly.

Speakers of regional or “non-standard” English often find their words flagged as incorrect, their accents ignored, or their syntax marked as a mistake. And that’s not just inconvenient—it’s exclusionary.

Why Mainstream American English Took Over

This dominance didn’t happen by chance. It’s historical, economic, and deeply structural.

The internet was largely developed in the US. Big Tech? Still mostly based there. The datasets used to train AI? Scraped from web content dominated by American media, forums, and publishing.

So, whether you’re chatting with a voice assistant or asking ChatGPT to write your email, what you’re hearing back is often a polished, neutral-sounding, corporate-friendly version of American English. The kind that gets labelled “standard” by systems that were never trained to value anything else.

When AI Gets It Wrong—And Who Pays the Price

Let’s play this out in real life.

• An AI tutor can’t parse a Nigerian English question? The student loses confidence.
• A resume written in Indian English gets rejected by an automated scanner? The applicant misses out.
• Voice transcription software mangles an Australian First Nations story? Cultural heritage gets distorted.

These aren’t small glitches. They’re big failures with real-world consequences. And they’re happening as AI tools are rolled out everywhere—into schools, offices, government services, and creative workspaces.

It’s “Englishes”, Plural

If you’ve grown up being told your English was “wrong,” here’s your reminder: It’s not.

Language is fluid, social, and fiercely local. And every community that’s been handed English has reshaped it, stretched it, owned it.

But many AI systems still treat these variations as noise. Not worth training on. Not important enough to include in benchmarks. Not profitable to prioritise. So they get left out—and with them, so do their speakers.

Towards Linguistic Justice in AI

Fixing this doesn’t mean rewriting everyone’s grammar. It means rewriting the technology.

We need to stop asking AI to uphold one “correct” form of English, and start asking it to understand the many. That takes:

• More inclusive training data – built on diverse voices, not just dominant ones
• Cross-disciplinary collaboration – between linguists, engineers, educators, and community leaders
• Respect for language rights – including the choice not to digitise certain cultural knowledge
• A mindset shift – from standardising language to supporting expression

Because the goal isn’t to “correct” the speaker. It’s to make the system smarter, fairer, and more reflective of the world it serves.

Ask Yourself: Whose English Is It Anyway?

Next time your AI assistant “fixes” your sentence or flags your phrasing, take a second to pause. Ask: whose English is this system trying to emulate? And more importantly, whose English is it leaving behind?

Language has always been a site of power—but also of play, resistance, and identity. The way forward for AI isn’t more uniformity. It’s more Englishes, embraced on their own terms.

You may also like:

• How Singtel Used AI to Bring Generations Together for Singapore’s SG60
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• Or try out the free version of Claude AI by tapping here.