Cultural Biases in AI: How Your Prompts Might Be Making Things Worse

Part 3 of "Musing on AI - By Dr Ruchi Sinha" (26th March 2025)

As an experiment, I recently asked an AI tool to help me draft an email to a potential research collaborator in Japan, and what came back was so aggressively direct that my Japanese colleague (if I ever sent it to him) would have told me that it read like a demand letter rather than a friendly invitation. 🙈 (I did share the output with my Japanese colleague).

Many of us think we are being efficient and technologically savvy by using AI, when in reality, you may be amplifying cultural biases you do not even realize are at play.

As more of us rely on AI tools like Large Language Models (LLMs) for everything from writing reports to solving complex problems at work, we're unknowingly participating in a fascinating cultural tango – where both the AI's built-in biases and our own cultural prompting styles create outputs that might be... well, problematic.

The Secret Cultural Life of Your Favorite AI Tools

Here's something that doesn't make it into the glossy marketing materials: those impressive AI models we're all using? They've been raised on a steady diet of predominantly Western content. It's like sending your AI to a very specific type of finishing school where it learned to value extreme individualism, prefer masculine perspectives, and get uncomfortable with uncertainty.

Research by Masoud and colleagues (2025) found that popular LLMs like GPT have essentially absorbed these cultural dimensions from their training data, making them naturally inclined to produce outputs that reflect and reinforce these values – even when you're trying to use them for global and culturally diverse work.

And it gets even trickier with sensitive issues. A study by Sitaram et al. (2025) showed that misgendering happens frequently because of these embedded biases, especially in languages with strong grammatical gender distinctions. (As someone who works across multiple languages, this finding made me rethink so many of my cross-cultural communications!)

It's Not Just the AI – It's How You're Asking! 🔍

Here's where things get really interesting (and a bit uncomfortable if you're as self-reflective as I am): how we phrase our prompts to AI tools often reveals our own cultural assumptions and biases.

Think about it. If you're from an individualistic culture like the United States (raises hand sheepishly), you might frame your requests emphasizing personal choice or independence:

"Generate a report highlighting individual accomplishments and personal achievements"

Without realizing it, you're amplifying the model's already individualistic bias. Meanwhile, your colleague from a more collectivist culture might ask:

"Create a report that showcases team harmony and group success"

Now the AI is confused – it's trying to process a collectivist request through its individualistic lens, potentially creating outputs that miss the mark entirely.

We're all unintentionally asking AI to see the world through our cultural glasses – not realizing the AI is already wearing its own set of culturally-tinted lenses!

The "Oh No, What Have I Been Doing?" Moment 😱

If you're having a small crisis of confidence right now about all the potentially biased AI outputs you've been using, you're not alone. When I first discovered this research, I went back through dozens of documents I'd created with AI assistance and spotted these biases everywhere.

There was the performance review template that heavily emphasized individual contribution over team collaboration. The marketing materials that assumed Western cultural references would resonate globally. The training materials that used examples that wouldn't translate well across cultures.

Pro Tip: This moment of realization is actually gold – it means you're developing the awareness that's essential for more inclusive AI use! ⭐

The Recovering Cultural-Bias-Amplifier's Toolkit 🧰

So what can we actually DO about this tangled web of cultural biases? Based on both research and my own trial-and-error experiences, here are some practical strategies that have made a huge difference:

1️⃣ Become a Prompt Detective

Start paying attention to the assumptions hiding in your prompts. Are you unconsciously reinforcing individualism, masculinity, or other cultural dimensions?

I've started keeping a "prompt journal" (yes, I'm that nerdy) where I analyze patterns in how I ask for things. It's been eye-opening to see my own cultural biases laid bare in black and white!

Try this exercise: Take your last five prompts to an AI tool and ask yourself:

• What cultural values are embedded in my language?

• Would someone from a different cultural background phrase this differently?

• Am I assuming universal experiences that might not be universal?

2️⃣ Give Your AI Cultural Context

Instead of assuming your AI will magically understand cultural nuances, explicitly tell it what you need:

Before: "Write a business proposal for my potential partner."

After: "Write a business proposal for a potential partner in South Korea. Incorporate South Korean business communication norms such as acknowledging hierarchy, building relationships before discussing business details, and using more indirect communication styles."

Masoud's research (2025) shows that this kind of soft prompt tuning can significantly improve cultural alignment. It's like giving your AI a mini cultural sensitivity training before each task!

3️⃣ Make It a Team Sport

Some of my best AI outputs have come from collaborative prompt design with my culturally diverse team. Before using AI for important tasks, try running your prompts by colleagues from different cultural backgrounds.

I remember drafting a survey a couple of months ago with my research team and being stunned by how differently my South American (Columbian) colleague would have phrased the same questions to an AI. Her perspective completely transformed our approach – and ultimately made our research more inclusive.

4️⃣ Create Cultural Guardrails

Sitaram's research (2025) shows that developing guidelines or "guardrails" through participatory design significantly reduces biases like misgendering.

In my own work, I've created a simple checklist that I run through before submitting important prompts:

• Have I specified the cultural context if relevant?

• Have I checked for assumptions about individualism vs. collectivism?

• Have I considered how this might translate across different cultural contexts?

• Have I clarified expectations around directness vs. indirectness?

These guardrails have saved me from countless cross-cultural communication blunders!

From Cultural Bias to Cultural Intelligence

Here's what gives me hope: just by reading this article, you're already developing the awareness that's the first step toward more inclusive AI use. You're starting to see both the AI's cultural biases and your own – which puts you miles ahead of most AI users.

The next time you interact with an AI tool, try to notice the cultural dance that's happening. What assumptions is the AI making? What assumptions are you making? How might this interaction look different through another cultural lens?

I'd love to hear about your experiences navigating cultural biases with AI tools. Have you had any "aha moments" or developed strategies that work particularly well? Share them in the comments – I read every single one! 💭

P.S. If you're wondering whether paying attention to these cultural dynamics is worth the effort – trust me, it is! The last time I wrote to my Japanese colleague using culturally-informed prompts, he responded with genuine enthusiasm rather than polite confusion. Progress!

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