Abundant intelligences: placing AI within Indigenous knowledge frameworks

“Two‐Eyed Seeing”: An Indigenous framework to transform fisheries research and management

Abstract Increasingly, fisheries researchers and managers seek or are compelled to “bridge” Indigenous knowledge systems with Western scientific approaches to understanding and governing fisheries. Here, we move beyond the all‐too‐common narrative about integrating or incorporating (too often used as euphemisms for assimilating) other knowledge systems into Western science, instead of building an ethic of knowledge coexistence and complementarity in knowledge generation using Two‐Eyed Seeing as a guiding framework. Two‐Eyed Seeing ( Etuaptmumk in Mi’kmaw) embraces “learning to see from one eye with the strengths of Indigenous knowledges and ways of knowing, and from the other eye with the strengths of mainstream knowledges and ways of knowing, and to use both these eyes together, for the benefit of all,” as envisaged by Elder Dr. Albert Marshall. In this paper, we examine the notion of knowledge dichotomies and imperatives for knowledge coexistence and draw parallels between Two‐Eyed Seeing and other analogous Indigenous frameworks from around the world. It is set apart from other Indigenous frameworks in its explicit action imperative—central to Two‐Eyed Seeing is the notion that knowledge transforms the holder and that the holder bears a responsibility to act on that knowledge. We explore its operationalization through three Canadian aquatic and fisheries case‐studies that co‐develop questions, document and mobilize knowledge, and co‐produce insights and decisions. We argue that Two‐Eyed Seeing provides a pathway to a plural coexistence, where time‐tested Indigenous knowledge systems can be paired with, not subsumed by, Western scientific insights for an equitable and sustainable future.

Treating data like land — data sovereignty in the AI age - ICT

Artificial intelligence front and center at North America’s largest Indigenous tech conference

Preventing AI extractivism: the case for braiding indigenous data justice with ABS for stronger AI data governance

Artificial-intelligence systems are rapidly reproducing colonial extractivism by harvesting Indigenous linguistic, biometric, geospatial, and ecological data without consent, compensation, or accountability. Biotechnology offers a blueprint for curbing such practices: the Convention on Biological Diversity and its Nagoya Protocol obligate users of genetic resources to obtain Prior Informed Consent, negotiate Mutually Agreed Terms, and share benefits fairly. No comparable framework restrains the digital appropriation that underpins many AI products. Consequently, corporations and states monetize Indigenous knowledge systems under the banners of “open data” and “scientific neutrality,” eroding rights affirmed in the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP). In response to this rising risk of AI extractivism, we make the case for a binding, sui generis ABS protocol for AI data governance. First, through a series of case studies we demonstrate that AI extraction mirrors the colonial and biopiracy controversies that originally triggered Access‑and‑Benefit‑Sharing (ABS) rules in biotechnology. Second, we translate those rules into a digital register by braiding two Indigenous data‑governance frameworks—OCAP® (Ownership, Control, Access, Possession) and the CARE Principles (Collective Benefit, Authority to Control, Responsibility, Ethics)—inside the ABS triad of consent, terms, and benefit‑sharing. The resulting model grounds technical safeguards in relational accountability and Indigenous legal orders. Such an instrument would compel transparent negotiations with Indigenous rights‑holders, assign enforceable authority over data across the AI lifecycle, and require equitable redistribution of the economic value generated by models trained on Indigenous data. Embedding ABS principles into AI governance offers a decolonial pathway that centers Indigenous epistemologies, promotes ethical foresight, and transforms AI from a vehicle of digital colonialism into a space for algorithmic justice.

Indigenous Knowledges and Data Governance Protocol — Indigenous Innovation Initiative

Between March 2020 and March 2021, the Indigenous Innovation Initiative co-created the Indigenous Knowledges and Data Governance Protocol with the community, to guide how we collect and use Indigenous Knowledges and Data. Click on the image below to learn more about this Protocol. Since then, t

Indigenous Data Sovereignty (DDN3-A11)

This article explores Indigenous data sovereignty, identifies some of the data-related challenges faced by Indigenous Peoples and highlights the work done to overcome these challenges.

The Shared Patterns of Indigenous Culture, Permaculture and Digital Commons | David Bollier

Rarely have I read an essay that knits together some very different commons with such wisdom and depth. Joline Blais' 2006 essay, “Indigenous Domain: Pilgrims, Permaculture and Perl,” is a wonderfully insightful analysis that reveals the underlying unity and logic of commons principles. Her piece appeared in Intelligent Agent (vol. 6, no. 2), published by the Inter-Society for the Electronic Arts.

Digital Labor Platforms as Infrastructure of Extractivism

This chapter examines how digital platforms, as infrastructures of extractivism in contemporary artificial intelligence, enable corporations to appropriate data and labour while concentrating value in core regions such as Silicon Valley. It proposes platform extractivism as a framework linking commodification, epistemic dominance, and reproduction through territory. Drawing on digital labour studies, Latin American critical thought, and mixed-methods research with Venezuelan and Colombian data workers, the chapter discusses how AI depends on precarious platform labour to produce, clean, annotate, and maintain data. It further shows how annotation systems normalize culturally specific categories, especially US norms, as universal knowledge, reproducing colonial hierarchies. Finally, it highlights how data work is sustained by unpaid reproductive labour, community networks, and material conditions that make workers available for platform labour. Platform extractivism thus reveals AI as a labour-intensive and territorially embedded political-economic system under capitalism that perpetuates legacies of coloniality.

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

Animikii: Indigenous Technology for an Equitable Future

Animikii empowers Indigenous communities with culturally informed technology solutions like Niiwin—our groundbreaking tool for Indigenous data sovereignty and community empowerment.

Sacralizing Dominion: The Doctrine of Christian Discovery and the Religious Nationalist Imagination

White Christian nationalism presents itself as restoration—of a Christian nation, a moral order, a rightful hierarchy. This article argues that what it restores is the world-making rhetoric of the Doctrine of Christian Discovery (DoCD). Reading the fifteenth-century papal bulls, their sedimentation into United States law, culture, religion, and contemporary dominionist discourse as a single rhetorical formation, we show that the bulls’ vocabulary of discovery, vacancy, and civilization did not describe a world but constituted one: a moral-legal order in which Christian identity conferred sovereignty and racialized peoples appeared as obstacles to salvation and property. That order persists as hegemonic common sense, carried by legal precedent, curricula, and popular culture, and it is re-performed today in the territorial rhetoric of the New Apostolic Reformation and white Christian nationalism. Following Steven T. Newcomb (Shawnee/Lenape), repudiating the doctrine’s documents without dismantling its embeddedness within language and cognition leaves religious nationalism’s deepest resource intact. Against the discovery template, we set three counter-rhetorics—Indigenous theologies, Black theology, and womanism—and argue that desanctifying dominion requires conciliation, not reconciliation.

Book Review of Making All the World America

Steven T. Newcomb reviews Timothy Bowers Vasko’s Making All the World America: Native Information and the Doctrine of Discovery (University of Pennsylvania Press, 2026).

Computational hermeneutics: evaluating generative AI as a cultural technology

Generative AI (GenAI) systems are increasingly recognized as cultural technologies, yet current evaluation frameworks often treat culture as a variable to be measured rather than fundamental to the system's operation. Drawing on hermeneutic theory from the humanities, we argue that GenAI systems function as "context machines" that must inherently address three interpretive challenges: situatedness (meaning only emerges in context), plurality (multiple valid interpretations coexist), and ambiguity (interpretations naturally conflict). We present computational hermeneutics as an emerging framework offering an interpretive account of what GenAI systems do, and how they might do it better. We offer three principles for hermeneutic evaluation—that benchmarks should be iterative, not one-off; include people, not just machines; and measure cultural context, not just model output. This perspective offers a nascent paradigm for designing and evaluating contemporary AI systems: shifting from standardized questions about accuracy to contextual ones about meaning.

Architecting Trust in Artificial Epistemic Agents

Large language models increasingly function as epistemic agents -- entities that can 1) autonomously pursue epistemic goals and 2) actively shape our shared knowledge environment. They curate the information we receive, often supplanting traditional search-based methods, and are frequently used to generate both personal and deeply specialized advice. How they perform these functions, including whether they are reliable and properly calibrated to both individual and collective epistemic norms, is therefore highly consequential for the choices we make. We argue that the potential impact of epistemic AI agents on practices of knowledge creation, curation and synthesis, particularly in the context of complex multi-agent interactions, creates new informational interdependencies that necessitate a fundamental shift in evaluation and governance of AI. While a well-calibrated ecosystem could augment human judgment and collective decision-making, poorly aligned agents risk causing cognitive deskilling and epistemic drift, making the calibration of these models to human norms a high-stakes necessity. To ensure a beneficial human-AI knowledge ecosystem, we propose a framework centered on building and cultivating the trustworthiness of epistemic AI agents; aligning AI these agents with human epistemic goals; and reinforcing the surrounding socio-epistemic infrastructure. In this context, trustworthy AI agents must demonstrate epistemic competence, robust falsifiability, and epistemically virtuous behaviors, supported by technical provenance systems and "knowledge sanctuaries" designed to protect human resilience. This normative roadmap provides a path toward ensuring that future AI systems act as reliable partners in a robust and inclusive knowledge ecosystem.

Commodity Intelligence

The seductiveness of “general intelligence” is rooted in a costly category error

Socially Minded Intelligence: How Individuals, Groups, and Artificial Intelligence Can Make Each Other Smarter (or Not)

A core part of human intelligence is the ability to work flexibly with others to achieve goals. The incorporation of artificial agents into human spaces is making increasing demands on artificial intelligence (AI) to demonstrate and facilitate this ability. However, this kind of flexibility is not well understood because existing approaches to intelligence typically construe this either as an individual-difference trait or as a property of groups. We argue that by focusing either on individual or collective intelligence without considering their dynamic interaction, existing conceptualizations of intelligence limit the potential of people and AI systems. To address this impasse, we propose a new kind of intelligence, 'socially minded intelligence', that can be applied to both individuals and collectives. We outline how socially minded intelligence might be measured and cultivated within people, how it might be modelled in AI agents, and how it might be applied to other intelligent systems.