Validating AI Model Generalization Across Languages
Completed:
This award-winning project tackled the multi-million-dollar risk for any AI company with global ambitions: product failure due to biased training data.
The core finding proved that a “one-size-fits-all” model, trained only on English, is not a viable strategy for international deployment. Performance across unseen languages was unpredictable, revealing significant hidden risks. This work produced a clear framework for de-risking global AI launches by validating the need for market-by-market testing and data localization, while reaffirming the need to thoroughly verify claims of multilingual capabilities across AI offerings.
Recognition
- Honorable Mention, University of Illinois Undergraduate Research Symposium
Tech & Skills
- Core Competencies: AI Bias & Fairness, Data-Driven Risk Analysis, Model Generalization, Go-to-Market Strategy