A Global First: How a New Sustainable Data Framework & License Are Transforming Language AI
- AI systems are only as effective as the data they are built on, and sustainable data practices have become a critical factor in how language AI scales globally.
Despite rapid advances in artificial intelligence, many African languages remain underrepresented in production-grade AI systems. A key reason is the absence of data frameworks that support efficient, ethical, and repeatable dataset creation under real-world constraints..
Lelapa AI, in collaboration with Way With Words and Data Science for Social Impact (DSFSI), has introduced the Esethu Framework, a data governance and licensing approach designed to support long-term, scalable development of low-resource language datasets. The framework rethinks how language data is created, maintained, and reinvested into future AI systems.
The Esethu Framework addresses structural inefficiencies in how low-resource language data is sourced and reused. By aligning ethical governance with sustainable commercial pathways, it enables dataset creation that supports both responsible use and long-term system development.
What is the Esethu Framework?
The Esethu Framework is a sustainable data curation and licensing model designed to support repeatable, cost-aware dataset development while giving language communities clear governance over how their data is used.
Key features include:
- Sustainable Licensing: The Esethu License introduces a community-aware commercial pathway that enables responsible use of language data while ensuring reinvestment into future dataset creation. This supports long-term availability of high-quality language data without repeated extraction cycles.
- Community-Led Development: Local linguists and native speakers play a key role in dataset creation, making sure the data is authentic and diverse.
- Scalability & Replicability: The framework is designed to be applied across multiple low-resource languages, enabling consistent dataset development processes that can scale across regions and use cases.
“Lelapa AI has developed a data framework that supports sustainable language AI development at scale. It enables ethical commercial use while ensuring that revenue flows back into future dataset creation and system improvement,“ says Jenalea Rajab, Research Lead at Lelapa AI.
A Proof of Concept – Introducing The ViXSD Dataset
As the first dataset developed under the Esethu Framework, the Vuk’uzenzele isiXhosa Speech Dataset (ViXSD) serves as a practical demonstration of how sustainable data governance supports efficient AI development. ViXSD is an open-source Automatic Speech Recognition (ASR) dataset designed for real-world deployment scenarios.
- 10 hours of high-quality isiXhosa speech data
- Diverse speakers across dialects, age groups, and regions
- Ethical licensing that ensures future isiXhosa data growth
This dataset opens new opportunities for building voice AI, transcription tools, and multilingual natural languages processing (NLP) models that better serve isiXhosa speakers.
But this is just the beginning! The Esethu Framework ensures that the same process can be applied to build datasets for other African languages, ensuring long-term, sustainable language AI development across the continent.
Why This Matters for African AI & NLP
Sustainable data practices are becoming a defining factor in how language AI systems perform, scale, and remain viable over time. The Esethu Framework introduces a model that aligns data governance with system efficiency and long-term development goals, bringing:
- Better AI for low resource Languages: More data means better speech recognition, multilingual assistants, and voice technology for users across Africa and the Global South.
- A Self-Sustaining Ecosystem: Licensing fees from non-African companies fund the creation of even more datasets for underrepresented languages.
- AI with True Inclusion: It’s time for low resource languages to drive AI innovation, not just be an afterthought.
Access the Resources
- Access the Esethu License
- Download the ViXSD Dataset
- Explore the Esethu Framework & Research Paper
By combining responsible data governance with scalable system design, the Esethu Framework contributes to a growing shift towards resource-efficient language AI. It demonstrates how data, when structured intentionally, can support both ethical use and global deployment of language technologies. Share this initiative to redefine how AI serves low resource languages!