The current trajectory of artificial intelligence development suffers from fundamental epistemological shortcomings, resulting in the systematic operationalization of bias against non-white, non-male, and non-Western peoples. We argue that these failings are, in part, the result of certain Western rationalist epistemologies that exclude many ways of knowing about the world, and therefore they cannot provide a sufficient foundation on which to adequately, robustly, and humanely conceptualize intelligence. We present a new research agenda, Abundant Intelligences, an Indigenous-led, Indigenous-majority international, interdisciplinary research program that imagines anew how to conceptualize and design artificial intelligence (AI) based on Indigenous knowledge (IK) systems. Abundant Intelligences draws on the rich plurality of Indigenous knowledge systems, bringing together diverse sets of thought, culture, and protocol together. We show IK systems provide one way to rebuild AI’s epistemological foundations and transform these tools’ current role in reinforcing colonial practices of exclusion, extraction, manipulation, and eradication into engines of abundance that enable us to care better for ourselves, our communities, and our world. Our proposition is to fully engage with AI to explore how different conceptions of intelligence could be embodied in these technologies. In this paper, we present the tenets of the research program in detail, account for our methodological approach, describe the impact and limitations, and conclude on a discussion of the implications of the program.
Solidarity Syllabus: Indigenous Peoples, Rematriation, and Resisting AI Data Centers — Solidarity Is
For this Solidarity Syllabus, we are learning alongside Indigenous organizers, leaders, and communities whose work offers important solidarity lessons and invitations for how we might understand land, water, sovereignty, and resistance to data centers. Indigenous Peoples Day is observed
identifAI - Discover Origin. Reveal Insight
identifAI pioneers discerning human-AI creations, bolstering authenticity with advanced models. Determine if an image come from AI or from human!
Sovereignty Is Not Solitude: Open Source as Canada’s Third Path in AI
True artificial intelligence (AI) sovereignty isn't about building a Canadian fortress; it’s about mastering the open-source ecosystems that power the world.
What a community taught an AI about itself: Conservation Technology with Arte & Escola na Floresta
By Nurfatin Hamzah, Diego Rivera, David Dao & Arte & Escola na Floresta
Field Theory: AI as Social Science Question, Object & Tool
Uses of advanced artificial intelligence are changing how societies organize labor, govern, produce knowledge, and make meaning. In light of these developments, this essay argues that AI models, tools, and systems pose three interrelated imperatives for social science: they demand renewed attention to social theories of how technology, human experience, and social order are entangled; they require study as objects of inquiry in their own right; and they offer capabilities that may transform—or upend—the practice of social investigation itself. From Weber’s analysis of rationalization to Du Bois’s study of technology and inequality to contemporary scholarship on algorithmic governance, the essay examines what social science distinctively offers: the capacity to historicize the apparently unprecedented, to trace connections across scales, and to center those most affected by technological change. It identifies how algorithmic systems are remaking the distribution of opportunity and risk as a central task of social inquiry and asks what futures social science might help bring into being.
AI Is a Product of Humanity. Humanity Should Own AI.
Big Tech has looted humanity’s creative output to create artificial intelligence. We don’t just deserve a share of AI’s profits — we deserve control over it.
Unlawful by design: Exposing the human rights costs of generative AI - Amnesty International
This briefing examines how standalone generative AI systems, based on unlawful web scraping, are in conflict with international human rights law (IHRL) and standards through their design, development and deployment. While these technologies promise sophisticated automation and efficiency, they rely on data collection and model training practices that abuse privacy rights, enable discrimination, and threaten […]
On AI and building your own GIS software
Of the thousands of essays over the past two years surrounding AI and its implications are some very thought provoking ones with enormous implications for GIS. Some of the essays are around the the…
The Missing Institution: A Global Dividend System for the Age of Transformative AI — Digitalist Papers
How do we share prosperity in society if work no longer sits at the center of people’s lives? In this essay, Anna Yelizarova explores one provocative possibility: a “global dividend.” If transformative AI shows little respect for national borders, with effects that spill across countries, then we ne
The AI Pledge for Humanity
We all made AI possible. We all deserve a share of what it makes possible.
More Than a Tool
What We Learned from a Year of Conversations About AI
✨🙌 AI that helps communities thrive on their own terms
Open protocols + AI-enabled coding = building what we need for ourselves
Education's Broken. Schools Out. | Systematic-X × Shared Mind
A unified movement for systemic change — integrating diplomatic architecture, open frameworks, and sovereign data models. Systematic-X × Shared Mind.
A Short Guide to Data Strikes and Conscious Data Contribution in the Context of 2026 Frontier AI
Back to the basics of data leverage.
Wikipedia:WikiProject AI Cleanup
Welcome to WikiProject AI Cleanup, a collaboration to combat the increasing problem of poorly written AI-generated content on Wikipedia. If you would like to help, add yourself as a participant in the project, inquire on the talk page, and see the to-do list.
KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment
Maintaining comprehensive and up-to-date knowledge graphs (KGs) is critical for modern AI systems, but manual curation struggles to scale with the rapid growth of scientific literature. This paper presents KARMA, a novel framework employing multi-agent large language models (LLMs) to automate KG enrichment through structured analysis of unstructured text. Our approach employs nine collaborative agents, spanning entity discovery, relation extraction, schema alignment, and conflict resolution that iteratively parse documents, verify extracted knowledge, and integrate it into existing graph structures while adhering to domain-specific schema. Experiments on 1,200 PubMed articles from three different domains demonstrate the effectiveness of KARMA in knowledge graph enrichment, with the identification of up to 38,230 new entities while achieving 83.1% LLM-verified correctness and reducing conflict edges by 18.6% through multi-layer assessments.