Indigenous languages are critically endangered. According to the United Nations, an Indigenous language is lost approximately every two weeks. Languages can fade naturally as their final speakers pass on, but many Indigenous languages have vanished even as their people survive, pressured by the spread of majority languages. For example, Japan’s Indigenous Ainu population has faced forced assimilation through compulsory government schooling programs and discrimination that discouraged the passing down of the language. Families hid their Indigenous identity, sometimes not disclosing their heritage to their own children. Today, the Endangered Languages Project estimates that only two native Ainu speakers remain.
Indigenous Recognition, Reclamation, and Revival
Indigenous people around the world are reclaiming their heritages and developing new ways to utilize technologies to preserve their languages and cultures. Some creators are producing Indigenous small language models (SLMs). The historical undervaluing of Indigenous languages means that they are not commonly found in online audio or text sources, so creators have to build their own language corpus with the help of remaining native speakers and community re-creations.
Credit: Monbeeree, CC BY-SA 4.0, via Wikimedia Commons
Language: A Cultural Resource To Be Shared, Not Exploited
Indigenous creators want their communities to own the data used in Indigenous language models so they cannot be copied and sold by larger corporations or government agencies without permission, a concern based on the historical traumas faced by many Indigenous populations. An NPO consortium to develop a Lakota dictionary (a language Indigenous to North America) had copyrighted audio recordings taken of community members, preventing their families from accessing and utilizing the materials. By building these models and technologies within their communities, it ensures data sovereignty. This protects their data from predatory companies and ensures access for the very people who play such a large part in creating it.
Today, Indigenous researchers, coders, and creators have taken on that challenge directly, inventing new ways to empower future generations of Indigenous language learning with AI. Researchers in México at the Technological Institute of Oaxaca created an AI app that could identify and describe photographed items in the Tu’un Savi language. Coto Solano, an assistant professor at Dartmouth College in the U.S., worked to build an AI model that could identify and transcribe recordings in the Cook Islands’ Māori language. Beyond software, Anishinaabe inventor Danielle Boyer and her team in the U.S. have created an AI language-learning robot called SkoBots. Skobots use AI to identify spoken English questions and verbally respond in the Anishinaabemowin language.
Righting Historical Wrongs
Preserving Indigenous languages preserves cultural identities, worldviews, traditions, and knowledge. This state of loss was caused by discriminatory practices and pressures to conform to the majority languages and cultures. SLMs implemented in language apps, devices, and robots like SkoBots are a powerful tool to combat the decline of Indigenous languages, but this work should not be on Indigenous communities alone. Institutions besides private companies, like universities, can take steps to directly provide courses and technology for students to learn. Michigan State University, for instance, has led a Less Commonly Taught Languages program for more than a decade, including Anishinaabemowin and Quechua (a language Indigenous to South America).
The majority must stand as allies, advocating for better treatment of Indigenous populations and encouraging the use of these tools to spread awareness of Indigenous languages and cultures to the broader global community without infringing on data sovereignty. We must work together to keep these languages alive. If we fail, so much history and culture will be lost to humanity forever.
Credit: Joan San Carlos, CC BY-SA 4.0, via Wikimedia Commons
This article was written by a guest contributor, S. Jarvis.
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