Generative Simulation Benchmarking for heritage language revitalization programs with ethical auditability baked in
I still remember the morning I first realized the profound gap between our AI capabilities and the needs of endangered language communities. It was 3 AM, and I was staring at a terminal window, watching a transformer model generate synthetic Navajo verb conjugations. The model was producing grammatically perfect forms—something that would have taken a human learner months to master. But as I cross-referenced the outputs with a digital archive of elder recordings, I noticed something troubling: the model was systematically avoiding certain dialectal variants, particularly those from the Western Apache influence zone. Without realizing it, the model was performing a kind of linguistic erasure, privileging the standardized "school" dialect over the living, breathing diversity of the language as actually spoken.
That night, I began a personal journey that would lead me to develop what I now call Generative Simulation Benchmarking (GSB) —a framework that doesn't just measure model performance on heritage language tasks, but bakes ethical auditability into every layer of the evaluation pipeline. This article shares what I learned from that journey, the technical architecture that emerged, and the hard-won insights about what it truly means to revitalize a language through AI without harming the very communities we aim to serve.
