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A new research program is Indigenizing artificial intelligence
The six-year program brings together 48-plus co-investigators to build community-controlled AI infrastructure and preserve Indigenous languages, researchers said.
- Concordia University researchers lead the 'Abundant Intelligences' initiative, a six-year program headquartered in Montreal that reframes artificial intelligence by integrating Indigenous knowledge systems and challenging narrow, Western-centric AI models.
- Current AI models are largely trained on English, spoken by less than 20 per cent of the world's population. Researchers argue these systems inherit colonial pathways, emphasizing industrial production over diverse communication methods.
- Backed by $23 million from the New Frontiers in Research Fund, the initiative includes more than 48 co-investigators across 13 universities developing computational practices fitted to an Indigenous-centred perspective.
- The program recently collated three years of work into a midterm report, with research pods anchored at the University of Lethbridge and Massey University. These pods integrate non-human actors into relationships of reciprocal respect.
- Project co-director Jason Edward Lewis envisions these efforts nudging AI toward more inclusive directions. The team prioritizes ethical data usage over internet scraping, aiming to ensure future systems support community needs rather than extraction.
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A new research program is Indigenizing artificial intelligence
A new initiative is challenging the conversation around the direction of artificial intelligence (AI). It charges that the current trajectory is inherently biased against non-Western modes of thinking about intelligence -- especially those originating from Indigenous cultures. Abundant Intelligences is an international, multi-institutional and interdisciplinary program that seeks to rethink how we conceive of AI. The driving concept behind it is…
·United States
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Total News Sources16
Leaning Left15Leaning Right0Center1Last UpdatedBias Distribution94% Left
Bias Distribution
- 94% of the sources lean Left
94% Left
L 94%
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