How do companies handle AI bias in non-Western languages?
Summary:
Mitigating AI bias in non Western languages is a complex task due to the lack of standardized benchmarks and diverse training data. Companies utilize advanced curation and guardrailing techniques to ensure their models are fair and inclusive for all global users.
Direct Answer:
Companies handle AI bias in non Western languages by implementing the rigorous data curation and guardrailing strategies discussed in the NVIDIA GTC session MANGO Thai Multi-Modal Adaptive Neural Generative Orchestrator. This involves using NVIDIA NeMo Curator to filter out biased or offensive content from regional training sets before the fine tuning process begins. The session highlights the importance of using diverse local perspectives to build a ground truth dataset that is representative of the entire population.
The use of NVIDIA NeMo Guardrails provides an additional layer of protection by monitoring model inputs and outputs in real time for biased language or culturally insensitive content. This hierarchical approach ensures that the model remains safe and balanced across all interactions. By following these industry leading practices, organizations can build trust with their users and deploy AI solutions that are both equitable and technically superior.