Mirela
We know that localization comes with its fair share of problems, which we can luckily avoid or fix easily. However, as AI became more integrated in localization workflows, we began seeing new issues emerge—AI-related issues. One of the most common is bias. Bias can mean many things, and it’s not limited to translation quality. If you want to learn how to reduce bias in AI localization, keep reading. In this article, you’ll discover what bias means in this industry, and steps you can take to minimize it.
What bias looks like in localization
If you use AI to localize content, bias can distort meaning, reinforce stereotypes, exclude cultural groups, and create inconsistent user experiences.
Gender bias
AI systems sometimes make assumptions about a person’s gender based on stereotypes rather than the information provided in the source content. We call it gender bias. The issue is not just with pronouns; AI systems may also associate leadership positions with men, portray women more frequently in support roles, generate different descriptions for men and women, although they perform the same task, and use gendered language when neutral alternatives are available.
Cultural bias
Many AI systems are trained primarily on content originating from North America and Western Europe. It’s not surprising that they might treat Western perspectives as the default. Cultural bias may mean references of holidays that are not celebrated in the target market, recommending products that are not used in the target region, or naming celebrities that are unfamiliar to local audiences.
Linguistic bias
Linguistic bias is a result of unequal language representation in training data. Languages with large amounts of digital content receive far more attention during model development than low-resource languages. Because of this, AI systems perform better in English or Spanish compared to languages with fewer online resources.
Socioeconomic bias
AI system can also make assumptions about income levels, education, technology access, and lifestyle patterns. Much of the online content originates from wealthier and more digitally connected populations, so AI systems often learn a narrow view of how people live and interact with technology.
This is why you might get content that assumes users have access to high-speed internet, own smartphones, use credit cards (or digital payment methods), or simply have advanced digital literacy. But we can’t assume all this. These assumptions can lead to content that’s less useful, less accessible, and less relevant to large portions of the target audience.
Actual steps to reduce bias in AI localization
There’s no single solution. Check out the following suggestions, do the work, and create your own bias-reducing recipe:
Diversity training and fine-tuning data
There’s no better way to reduce bias in these systems than to fine-tune your models with the right data. If you use a dataset that overwhelmingly represents certain regions, cultures, demographics, or viewpoints, the model will naturally learn to prioritize those perspectives. So make sure to evaluate whether your training data accurately reflects the audiences you intend to serve.
Build inclusive terminology guidelines
You probably already use glossaries and style guides for consistency. However, these resources can also be used as tools for ensuring inclusivity. You can actually use them to establish clear expectations for how sensitive topics, demographic groups, identities, and professions should be represented. Why leave these decisions to AI systems?
Use human review
Humans are best at identifying and correcting bias. Sure, you can use automated systems, but these detect only certain linguistic issues. They’re not good with cultural nuances, social sensitivities, and contextual factors that influence how content is perceived. In the best case scenarios, your reviewers have direct knowledge of the target market. People who live within a culture are better equipped to recognize subtle issues.
Benchmark across languages
Some languages are more affected by bias than others. Don’t be surprised if you get highly inclusive and culturally appropriate content in one language and problematic outputs in another. This is why you need to compare equivalent content across language pairs to see if there are any discrepancies in tone, inclusivity, cultural adaptation, and overall user experience.
Create bias-specific evaluation metrics
General localization metrics are good for evaluating translation quality, but in this case, you need additional ones. The reality is that, even though a translation can score high for fluency and accuracy, it can still contain biases. Take the time to develop evaluation criteria specifically designed to measure your bias-related concerns.
Prompt engineering
It might be surprising that we left this last, but the thing is—prompt engineering can only do so much. It’s often portrayed as a solution for reducing bias in AI localization, but prompts do not fundamentally change the data or patterns the model has learned during training. So yes, you should definitely provide clear instructions on how the AI should use inclusive language, but don’t rely entirely on prompts.
Wrapping up
Future developments in AI localization will improve the outcomes. For now, however, we need human oversight. Bias can be reduced with the right approach: using diverse datasets, establishing inclusive language guidelines, and testing outputs across different demographics and languages. The trick is to catch any potential issues before they reach your audience and continue to improve your processes.