Abundant intelligences: placing AI within Indigenous knowledge frameworks

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.

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.

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.

A guide to the AI tribes

A primer on the ideological camps shaping the race to AGI

Building a Solidarity Ecosystem for AI (SSIR)

How cooperatives, public institutions, and social movements can come together to intentionally build a practical, community-owned alternative to extractive AI systems. <meta property=

Large AI models are cultural and social technologies

Implications draw on the history of transformative information systems from the past , Debates about artificial intelligence (AI) tend to revolve around whether large models are intelligent, autonomous agents. Some AI researchers and commentators speculate that we are on the cusp of creating agents with artificial general intelligence (AGI), a prospect anticipated with both elation and anxiety. There have also been extensive conversations about cultural and social consequences of large models, orbiting around two foci: immediate effects of these systems as they are currently used, and hypothetical futures when these systems turn into AGI agents—perhaps even superintelligent AGI agents. But this discourse about large models as intelligent agents is fundamentally misconceived. Combining ideas from social and behavioral sciences with computer science can help us to understand AI systems more accurately. Large models should not be viewed primarily as intelligent agents but as a new kind of cultural and social technology, allowing humans to take advantage of information other humans have accumulated.

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!

Ali Alkhatib: Defining AI

The main issue I have with a lot of work that tries to define AI is that the criteria they use to draw boundaries often turn out to be functionally useless for my needs; these definitions lead us to weird places, letting scholars fixate on strange, unworkable frameworks. Those pedantic fixations don’t really benefit the organizers, activists, regular people who are getting crushed by the systems they’re trying to work against. So I’m going to try to unpack how I think about AI; how I trace the boundaries of the term in a way that’s as useful as possible for me and my needs; and how I would encourage you to scope or define ideas that are important to your work.