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Stranger Danger: How Well Do You Know Your AI?
Global street smarts through culturalization.
Case Study: Multilingual Retail Marketing
New AI Content Creation Solutions for a Sports and Apparel Giant
- RESOURCES
"Take a moment to reflect on what your point of view is in engaging with a machine. And then tell the machine, here's our shared point of view.… If you do some before-and-after [work], you'll be pleased with the results."
—Marcus Casal, Lionbridge Chief Technology Officer
Artificial intelligence (AI) is transforming how global brands create content, drive customer engagement, and scale their marketing strategies. But as businesses lean into Large Language Models (LLMs) and generative AI, a crucial question arises: How well do you know your AI, including its biases and its challenges in delivering culturally relevant content?
During our webinar, “Stranger Danger: How Well Do You Know Your AI?”, Marcus Casal, Lionbridge’s CTO; Will Rowlands-Rees, Lionbridge’s Chief AI Officer; and Detria Williamson, AI 2030’s Chief Experience Officer, tackled this question head-on. Their goal? To help global leaders, marketers, and technologists recognize and stop bias in AI systems and achieve effective culturalization to protect brand reputation.
Want to watch the webinar in its entirety? Use the button below to access the recording.
What Is AI Bias?
AI bias occurs when AI systems produce outcomes that reflect and reinforce prejudices found in their training data or design. These biases can be subtle or overt, influencing everything from the images an AI generates to the language it uses in global marketing content. Recognizing that AI bias exists is the first step toward mitigating its impact and ensuring your content resonates with diverse audiences.
What Is Culturalization?
Culturalization is the process of adapting content and messaging to reflect the unique values, preferences, and norms of a specific audience or market. While AI can generate content for global audiences, it may struggle to capture the cultural nuances that make messaging truly resonate. By incorporating culturalization into your AI strategy, you ensure that outputs go beyond basic translation or accuracy. Instead, the output connects authentically and respectfully with people from diverse cultures, strengthening your brand’s global impact.
How Can AI Bias and a Lack of Culturalization Negatively Impact My Brand?
When companies depend on AI for global content, biased or culturally insensitive output can undermine brand reputation and trust.
These types of outputs can be tone-deaf, awkward, and misrepresentative of your target audience. Such issues may alienate potential and existing customers, create negative brand perceptions, reduce marketing effectiveness, and damage brand integrity.
What Are Four Key Factors Shaping AI-Generated Content?
Our experts identified four main factors that can impact AI-generated content and result in output that is biased or culturally misaligned:
Your point of view: If you don’t clearly define your brand’s perspective and audience when interacting with AI, the system will fill in the gaps with its own assumptions. This lack of clarity can lead to generic, irrelevant, or culturally insensitive content.
Training data bias: AI learns from the data it’s fed. If training data overrepresents certain cultures, languages, or demographics, the outputs may reflect those biases and exclude or misrepresent parts of your target market.
Prompting strategy: The way you frame prompts matters. Vague or non-specific prompts lead the AI to rely on statistical likelihoods, which can reinforce stereotypes or overlook essential cultural context. Hyper-specific prompt chains help deliver more relevant and inclusive results.
The nature of language: Every language handles gender, formality, slang, and idioms differently. AI can struggle with these nuances, sometimes producing translations or messaging that unintentionally shift tone, lose meaning, or cause offense.
Each of these factors influences how AI interprets tasks and generates content, and ultimately affects your brand’s cultural relevance and reputation.
How Does AI Bias Show up in Practice?
Breaking down the entrepreneur example
You can find many examples of bias in AI. When webinar presenters prompted Gemini to generate an image of a “successful entrepreneur,” the result reflected how bias can surface in the outputs.
As shown in the image above, the output was a white woman in a business suit working in a high-rise city office.
The presenters broke down the reasoning behind each detail, revealing how training data and default assumptions about success, professionalism, and environment shaped the AI’s choices. For example, the AI selected a woman to challenge traditional gender stereotypes but defaulted to a white individual in a corporate setting, reflecting the overrepresentation of white individuals and corporate settings in its dataset.
Without clear direction, AI systems tend to reinforce familiar patterns, which may not reflect your audience or brand values.
Examples of gender & formality and its impact on culturalization
Language is a social construct, and its nuances play a crucial role in effective culturalization. English is unusual among major languages in lacking gendered nouns and formal address, but that’s not the case in global markets. The webinar explored how gender and formality in languages, such as Spanish, French, and Arabic, can dramatically shift brand tone and respectfulness, and ultimately customer perception, making culturalization essential.
For instance, Spanish marketing content can use “innovador” (masculine) or “innovadora” (feminine) to describe the brand “Apple.” The way AI translates this may make the brand appear to be more masculine or feminine than intended. Similarly, using “tu” (informal Spanish) when “su” (formal Spanish) would be more appropriate can make your message sound disrespectful or overly casual in specific contexts, such as the banking sector.
These examples illustrate how overlooking culturalization in AI-generated content can result in messaging that fails to connect, undermines trust, and weakens your brand’s impact.
How Can Global Brands Stop AI Bias and Deliver Culturally Relevant Content?
Webinar experts made it clear that addressing AI bias and achieving culturally relevant content is easier than you think.
Start by auditing your content and AI outputs for both bias and culturalization gaps. Build your approach around proven best practices, such as refining your prompts, ensuring your training data is representative, and actively managing cultural nuances and brand tone. The webinar outlined proven ways to create bias-free, culturally relevant content.
Actionable strategies to prevent AI bias and achieve culturalization:
Point of view: Be explicit about your goals, audience, and desired outcomes with your AI.
Training data bias: Define your point of view and ensure your data set accurately represents that at a sufficient scale.
RAG (Retrieval-Augmented Generation): Leverage glossaries, style guides, and brand voice into RAG to provide AI with the correct context.
RLHF (Reinforcement Learning from Human Feedback): Validate outcomes with ongoing human-in-the-loop review for continuous model improvement.
Prompting: Be hyper-specific in your prompting chains so that nothing is left to inference.
Cross-language understanding: Conduct linguistic audits, collaborate with experts, and develop style guides for each market.
How Can Lionbridge Help?
With Lionbridge, your organization can confidently leverage AI to reach new audiences, strengthen brand reputation, and achieve global success, while keeping cultural awareness and sensitivity at the core of your content strategy.
We can help you:
Clarify your strategy and approach: Define your point of view and ensure your AI systems and content are culturally relevant for your target audiences.
Collect comprehensive training data: Source and curate diverse, representative datasets (including images, video, audio, and motion) to support your AI initiatives with our responsible AI data services.
Optimize retrieval-augmented generation (RAG): Structure, clean, and host linguistic data; vectorize content; and create patterns for high-quality, multilingual outputs aligned with your brand voice.
Validate and refine outputs: Deliver ongoing validation and human-in-the-loop review with a global community of over 500,000 experts to keep your AI models accurate and trustworthy.
Engineer effective prompts: Develop and refine hyper-specific prompt chains with our 5,000 multilingual prompt experts, and leverage the Lionbridge Content Remix App, our advanced multilingual content generation platform.
Ensure cross-language consistency: Conduct linguistic audits, collaborate with experts, develop style guides, and provide training for writers to mitigate bias and ensure consistency across markets.
With Lionbridge, your organization benefits from a proven partner dedicated to responsible AI, culturalization, and global brand integrity.
What Are the Key Webinar Takeaways?
This webinar explored how to identify and address bias in AI systems and ensure culturally relevant outputs, from ensuring the AI understands your point of view to obtaining adequate training data. Here are the key points:
AI bias is real: Stay aware of its existence.
Your point of view matters: Be explicit with AI about your audience and goals.
Training data matters: Ensure your datasets represent your market.
Prompting strategy is key: Use hyper-specific prompt chains for relevant, inclusive results.
Language nuances influence outcomes: Address gender, formality, and slang to shape brand tone and cultural resonance.
Accountability should be shared: Collaborate across marketing, technology, and brand leadership.
Validation is essential: Combine automated and human-in-the-loop review for responsible, trusted AI output.
Your Top AI Bias and Culturalization Questions Answered
A: Accountability should be shared across marketing, technology, and brand leadership. When these groups collaborate, AI strategies are more likely to reflect your organization’s values and audience needs. Leveraging diverse expertise strengthens outcomes by mitigating bias and ensuring cultural relevance.
A: Treating AI as only a tech issue is risky. Modern brand teams succeed by working in hybrid models. Marketers should proactively join the conversation, emphasizing cross-functional collaboration to lower brand risk and ensure AI-driven outcomes are relevant and safe.
A: Regularly update your training data and glossaries to reflect current cultural trends. Rely on brand ambassadors and cultural experts for review and use both automated checks and human oversight to keep messaging relevant and appropriate.
A: Before adopting new slang, the brand ambassador should ensure it aligns with brand values. If approved, the marketing, technology, and governance teams must collaborate to ensure safe, authentic integration. Although AI models can quickly learn new slang, establishing a clear perspective ensures your message remains authentic and appropriate.
A: Off-the-shelf models like Gemini, Llama, Claude, or GPT all perform well. The key is how users prompt and interact with the model. Making your perspective clear and customizing prompt chains to fit your needs are more important than the model you select.
If you are interested in exploring other Lionbridge AI-related webinar topics, visit the Lionbridge webinars page for a library of recordings.
Get in Touch
Ready for your brand’s global marketing messages to resonate with relevant content that embodies cultural awareness and cultural sensitivity? Reach out to learn how Lionbridge can help you leverage AI to your advantage while overcoming the risks of biases in AI.
Note: The Lionbridge Content Remix App initially generated this blog post, and a human writer refined it.
