Why AI Localization Matters

By Vincent Swan, Vice President of Technical Solutions
A woman wearing a hijab speaking into a mobile phone while working on a laptop in a home setting.

Products fueled by artificial intelligence, such as voice assistants, are becoming smarter and more pervasive around the world. At the same time, their proliferation raises concerns about bias. AI localization can play a crucial role in making AI-based products more inclusive and relevant to the needs of multiple markets.

AI Localization Defined

AI localization is about localizing AI products, tools, and services to work in their respective markets. Localizing AI means training AI with localized data – in other words, collecting and curating data sets that respond to cultures in different markets. In addition, localization experts increasingly use AI and machine learning engines as essential tools for enhancing end customer experience and driving customer satisfaction and loyalty.

In this context, localization is no longer about just translating files, text, and software for human consumption. Language translation is part of AI localization, but AI localization also includes localized experiences that people love no matter what country they live in – experiences that resonate based on their own cultures.

Ultimately, AI localization helps AI deliver memorable experiences. As localization specialists make voice assistants, chatbots, computer vision applications, and other AI-based products relevant to different markets, they have a tremendous opportunity to make AI more inclusive.

How Localization Helps AI Be Inclusive 

There is a perception that AI is created by a small group of data scientists who represent a narrow set of markets. Therefore, AI is built for a small group of people. But we really need to make sure that AI is accessible to as many people as possible through AI localization. And AI needs to be trained to address bias.

Localization practitioners do that at the highest level by opening up AI to different languages. But localization for inclusion goes way beyond that. For example, a product that uses computer vision needs to be able to understand different types of characters in multiple markets. A search engine must adapt to the context of how people expect information to be presented to them in different markets.

A big part of adapting AI to be inclusive is curating and building data sets that address multiple types of users. Doing so is complicated, but it starts with addressing user personas who are more diverse than the generic user.

These personas should be based on real-world data, such as demographics, behaviors, and preferences. By creating multiple user personas, developers can gain a more nuanced understanding of the needs and desires of different groups of people.

Once these personas are created, developers can use them to design and test AI systems that meet the needs of diverse users. This approach ensures that the AI system is not designed solely for a generic user but instead is tailored to the needs of specific user groups. It also helps to identify and mitigate potential biases in the AI system that might otherwise go unnoticed.

The Benefits of Localizing Data

Localizing data for AI models offers several benefits that can make a significant impact on the effectiveness and accuracy of these models. One of the primary benefits of localization is the ability to cater to the needs of a diverse range of users, regardless of language or cultural background. By localizing data, AI models can better understand the context, sentiment, and intent behind user data in a way that is more relevant and meaningful to the user.

Localization can also help AI models provide more accurate and relevant results. For example, a localized model trained to recognize food items in a specific region can provide more accurate results than a model trained on data from a different region. This is because the localized model is trained on data that is more relevant to the specific region, and therefore more likely to provide results that are accurate and useful to users.

Another benefit of localizing data for AI models is the ability to improve the user experience. A localized model can be trained to recognize and respond to different languages, date and time formats, and cultural norms, resulting in a more seamless and personalized experience for users. This can lead to increased user satisfaction and engagement, which is critical for businesses looking to expand their reach in new regions.

Finally, localizing data for AI models can provide businesses with a competitive advantage. By providing a localized user experience, businesses can differentiate themselves from their competitors and build brand loyalty among users. This can also help businesses to expand into new regions and tap into new markets that they may not have been able to reach otherwise.

Learn More

Today, and in the future, we leverage skills of new talents, next-gen AI, and a foundation of many years of experience refining global programs and quality management frameworks. To read more about localizing AI products, please read our in-depth analysis in the recently published Centific report, The State of Localization. Learn how Centific can help you succeed with localization by visiting our website, and contact us to get started.