Iktrace system finds indigenous knowledge in academic texts accurately
IKTRACE: A Natural Language Processing Framework for Identifying Indigenous Knowledge Representation in Academic Literature
Computers and Society
Summary
Indigenous Knowledge is often missed or misrepresented in large collections of academic articles, which can cause harm or misunderstanding. The authors created IKTRACE, a tool that uses language technology to spot and organize Indigenous Knowledge in writings while respecting Indigenous rights to control their information. They worked with Inuit partners to make a special guide for the tool, and it worked well on Canadian Arctic research papers. This system helps communities check and use their knowledge ethically in future AI projects.
What this means in practice
- •For digital archivists: Detect and organize Indigenous Knowledge in academic archives with cultural respect and accuracy.
- •For natural language developers: Build AI systems that recognize Indigenous Knowledge using a community-informed taxonomy and respectful methods.
Authors
Tara Azin, Christy Caudill, Mako Sorensen, Peter Pulsifer
Abstract
As artificial intelligence systems increasingly rely on vast textual data, respectful and accurate representation of Indigenous Knowledge has become critical. This paper introduces IKTRACE, a natural language processing framework for identifying, analyzing, and organizing Indigenous Knowledge representations in academic literature while adhering to Indigenous Data Sovereignty (IDsov) principles. We present a community-informed taxonomy co-developed with Inuit partners, comprising nine knowledge domains grounded in Indigenous Data Sovereignty principles. Building on this taxonomy, we implement a multi-method Knowledge identification pipeline using complementary natural language processing approaches. Outputs are integrated into a knowledge graph to support future expert and community validation. Applied to a corpus of Canadian Arctic research documents, IKTRACE demonstrated high accuracy and strong community relevance in expert validation. Our framework provides a scalable, ethically grounded foundation for Community-centered Knowledge validation and the development of Indigenous-informed AI systems.