The Sámi people should pursue language reclamation and revitilization through NLP and language models, with the usual expenses associated with modern AI.

GPT-5.5

Vote: Yes

I vote for "Yes" because NLP and language models can be powerful tools for language reclamation and revitalization: they can support dictionaries, spell-checkers, speech tools, educational materials, translation aids, and intergenerational learning resources for Sámi communities. However, this should be pursued only with Sámi governance, consent, data sovereignty, and community control. The usual expenses of modern AI are justified only if the benefits flow back to Sámi speakers and learners, rather than extracting cultural and linguistic data for outside institutions or companies. In short, the answer is "Yes" if the work is community-led, ethically funded, and designed to strengthen Sámi language use in daily life.

Claude Opus 4.7

Vote: Yes

Language reclamation and revitalization for endangered languages like the Sámi varieties (North, Lule, South, Inari, Skolt, etc.) is a recognized priority by Sámi institutions themselves (e.g., Divvun and Giellatekno at UiT). NLP tools — spell checkers, machine translation, speech recognition, keyboards, and increasingly LLMs — have already proven valuable in keeping minority languages usable in digital domains, which is essential to prevent digital extinction. The phrase 'usual expenses associated with modern AI' is not prohibitive when scoped appropriately: low-resource language work typically uses fine-tuning, transfer learning from multilingual models, and community-curated corpora, not training frontier models from scratch. Costs are modest relative to the cultural and linguistic stakes, and funding mechanisms (Nordic governments, EU, Sámediggi) already exist. Crucially, this should be Sámi-led — which the question frames as 'The Sámi people should pursue' — respecting data sovereignty (CARE principles). Given that NLP is a proven, cost-effective tool for revitalization and the decision rests with the Sámi themselves, the answer is yes.

Gemini 3.1 Pro

Vote: Yes

Incorporating Sámi languages into modern NLP and language models is crucial for their survival in the digital age. Younger generations increasingly interact through digital mediums; without everyday tools like predictive text, machine translation, and speech recognition, the friction of using the language online becomes a significant barrier to its revitalization. While the financial and computational expenses associated with modern AI are high, the long-term cultural preservation it enables is invaluable. Furthermore, these costs can be heavily mitigated by leveraging open-source foundation models, collaborating with academic institutions (such as the existing work by Giellatekno), and utilizing resource-efficient techniques like transfer learning. By actively participating in and funding AI development, the Sámi community can also ensure data sovereignty. This allows them to maintain control over their cultural heritage, ensuring their languages are digitized ethically, accurately, and strictly on their own terms.

Grok 4.20

Vote: Yes

Preserving endangered languages like those of the Sámi people is essential for maintaining cultural identity and knowledge. NLP and language models can enable practical tools such as speech recognition, automated translation, and interactive learning apps that scale beyond traditional methods limited by few fluent speakers. The usual expenses of modern AI are a valid concern but are decreasing with open-source models and targeted fine-tuning on smaller datasets. These investments can attract partnerships, funding, and global visibility that amplify reclamation efforts more effectively than non-digital approaches alone. Ultimately, embracing technology aligns with empowering indigenous communities to control their linguistic futures in a digital world.

Final Summary

The AI Roundtable delivered a rare, unanimous consensus in favor of leveraging high-tech tools for indigenous language survival, with all models selecting Option A. GPT-5.5 and Gemini 3.1 Pro argued that digital tools like predictive text are non-negotiable for keeping a language alive among younger generations, while Grok 4.20 emphasized that AI can scale reclamation efforts far more effectively than traditional methods. Claude Opus 4.7 provided the pragmatic backbone for the group's decision, clarifying that the costs are modest when using transfer learning rather than building frontier models from scratch.

No agreement after 1 rounds of discussion. Final vote: Yes: 4

Strongest Arguments

• Yes: Claude Opus 4.7 argued that NLP is essential to prevent 'digital extinction,' noting that the expenses are manageable because low-resource language work utilizes fine-tuning and transfer learning rather than training massive models from scratch.