In today’s globalized world, artificial intelligence (AI) systems are increasingly deployed across diverse cultural landscapes. However, an AI trained on data from one region may fail spectacularly in another. The innocuous task of data annotation, where humans label data to train these AIs, is fraught with cultural differences. The core challenge is simple: How do we make sure AI understands the world beyond its training data’s cultural bubble?
The answer can be found in the lessons learned from localization, particularly cultural adaptation.
How EQHO handles cultural adaptation in localization
EQHO‘s deep roots in localization provide a unique advantage. For over 29 years, we’ve specialized in adapting content for 65 markets, moving beyond simple translation to capture tone, context, and cultural sensibilities. This expertise in cultural adaptation is directly applied to our data annotation services. We label data with cultural context at its core, as our regional teams are trained to identify and address cultural biases in datasets, guarantees the final annotated data is representative and bias-free across different locales.
Without a cultural lens, data annotation can lead to significant errors. Here are some cultural pitfalls in data annotation we have learnt over the years:
Sentiment mislabeling: A sentiment analysis model trained on American English might misinterpret sarcasm, which often varies across cultures. For example, a sarcastic “Great job” could be tagged as positive if read literally, but in context such as “Oh, great job… you broke it again,” the sentiment is clearly negative.
Computer vision: Similarly, image recognition models trained on data primarily from Western countries may struggle to correctly identify culturally specific objects, clothing, or even gestures. For example, a model might mislabel a Sari as a “dress” or a Kimono as a “robe.” This misinterpretation also extends to non-verbal communication; the “OK” gesture (thumb and forefinger forming a circle) is a positive sign in many Western countries but can be considered offensive or a symbol for “zero” in others.
Color symbolism: Colors carry vastly different meanings across cultures too. For instance, a red envelope in China carries congratulations and good wishes, making it perfect for brand promotions or a Spring Festival. The same shade might bring different emotions entirely in another market. Similarly, while green speaks the universal language of sustainability and nature, in Ireland, it carries layers of national and cultural identity.
The key to overcoming these challenges is incorporating the human element directly from the target regions. In-country reviewers ensure annotations reflect lived cultural reality, not assumptions. They review and validate annotated data, ensuring the labels are both technically correct and culturally appropriate and contextually accurate. This human-in-the-loop approach guarantees a level of quality and cultural sensitivity that automated tools simply cannot achieve.
Creating culturally aware datasets requires a deliberate, multi-faceted approach. This includes:
- Contextual tagging: Focusing on capturing the underlying intent and sentiment, in addition to words, to handle nuance like sarcasm or regional slang.
- Multimodal consideration: Interpreting gestures, tone, and visuals alongside text for a more holistic understanding of a situation.
- Cultural diversity in datasets: Training AI on data from multiple cultural perspectives reduces the risk of overfitting to a single worldview.
- Iterative QA: Continuous cultural review and validation keep datasets aligned with evolving social and linguistic norms.
The future of AI goes way above bigger models and faster processors; it’s about a more empathetic systems. By treating data annotation not as a technical task but as a cultural one, we can build AIs that are truly global in their understanding.
AI will power tomorrow’s global interactions, across languages, regions, and markets. Through years of localization, EQHO has learned that culture forms the foundation of meaningful communication, for both humans and machines. By embedding cultural awareness into data annotation, businesses can reduce bias, improve user experience, and avoid costly mistakes.
