Unmasking Bias in AI: What IndoBias Tells Us About Language Models

Indonesia, with its more than 1300 ethnic groups and 700 indigenous languages, presents a unique challenge for large language models (LLMs). The diversity is both a blessing and a complication. Bias in these models, particularly in underrepresented languages, hasn't received the attention it deserves. Enter IndoBias, a comprehensive benchmark that aims to change that.

Why IndoBias Matters

Think of it this way: without a culturally-grounded benchmark, we can't fully assess the representational fairness of LLMs across such a linguistically rich region. IndoBias steps up by testing biases in Indonesian as well as Javanese, Sundanese, and Makasar languages. It's like putting a spotlight on the nuances that are often overshadowed by more dominant languages.

IndoBias uses two evaluation tracks. One is depth-oriented, focusing on how models handle contrastive pairs. The other is breadth-oriented, involving generation-based tests grounded in social science frameworks like SPI and O*NET. What does this tell us? Models, especially decoder types, show strong biases toward prototypical Indonesian sentences. But local languages, biases spike under categories like Ideology and Religion.

The Surprising Role of Pretraining

If you've ever trained a model, you know the data you use matters. IndoBias found that Common Crawl texts, used during pretraining, inject more bias than human-reviewed sources like Wikipedia. It's a wake-up call. While adding local languages to the pretraining mix generally ramps up bias, it raises an important question: Are we sacrificing accuracy for inclusivity?

Here's why this matters for everyone, not just researchers. When LLMs are biased, they perpetuate stereotypes and misunderstandings. A non-uniform Stereotype Polarity in model responses means that depending on the local context, you might get wildly different outputs for the same input. Not exactly what you want from AI, right?

Taking a Stand

Honestly, IndoBias underscores the critical need for more culturally-aware AI systems. It's not just about language. it's about respect and understanding. The analogy I keep coming back to is a conversation. If a model can't accurately reflect the diversity of Indonesian languages, how can it ever hope to engage meaningfully with billions around the world?

So, what's the takeaway? We need more than just technical solutions. We need a shift in how we approach bias in AI. If multinational companies don't start paying attention, they'll miss out on understanding a massive, diverse market. And that's a loss we can't afford.