Large Language Models, despite their promise, often reflect the biases present in the data they are trained on. This can lead to a range of discriminatory outcomes, from misinterpreting polite refusals in Japanese to reducing vibrant cultural festivals to stereotypes. Bias can also create barriers for minority language speakers in accessing essential services, for example a Spanish emergency AI failing to understand a Basque speaker’s distress call. Furthermore, language bias can silence marginalized voices and exclude local sellers from the digital economy, in case of a Southeast Asian shop owner whose business suffers due to an English-centric e-commerce platform. In India, for instance, facial recognition systems trained primarily on lighter skin tones have shown significantly lower accuracy in identifying individuals with darker skin, leading to potential misidentification and wrongful arrests within darker-skinned communities. Additionally, in India, where code-switching between English and local languages is common, AI models often struggle to accurately process or generate text that mixes languages. This can lead to reduced effectiveness of AI tools in multilingual societies, potentially marginalizing speakers who regularly engage in code-switching. These examples underscore the urgent need to address bias in AI development and ensure that these systems are fair, equitable, and inclusive for all.