WCRI2026

International Research Integrity Conference | NEXT CONFERENCE 2027

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.

Guest Post — Research Needs a Shared Standard for AI Disclosure - The Scholarly Kitchen

Today's guest post presents outcomes of the WCRI's (World Conferences on Research Integrity) Focus Track to address this gap in clarity and standardization of AI disclosure.

Abundant intelligences: placing AI within Indigenous knowledge frameworks

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.

Scientific sleuths come in from the cold

Research integrity investigators are starting to organize, but the field, and the people, remain idiosyncratic

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

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

Clear Skies – Your partner in research integrity

The tech industry hijacking of the AI ethics research agenda and why we should reclaim it

This paper reflects on the tech industry’s colonization of the AI ethics research field and addresses conflicts of interest in public policymaking concerning AI. The AI ethics research community faces two intertwined challenges: In the first place, we have a tech industry heavily influencing the AI ethics research agenda. Secondly, cleaning up after the tech industry has implied that we have turned to value-driven design methods to bring ethics to AI design. But by framing research questions relevant to a technical practice, we have facilitated the technological solutionism behind the tech industry’s business model. Therefore, this paper takes the first steps to reshape the AI ethics research agenda by suggesting moving toward an emancipatory framework that brings politics to design while, at the same time, bearing in mind that AI is not to be treated as an inevitability. As a research community, we must focus on the repressive power dynamics exacerbated by AI and address challenges facing the vulnerable groups seldom heard, despite the fact that they are the ones most negatively affected by AI initiatives.

Research Engineer, Cryptographic Trust Infrastructure

Research & build tools that let AI agents negotiate using confidential, verifiable data.

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

The Real Existential Risk Is AI CEOs Avoiding Oversight: “Alternative Nobel” Winner Timnit Gebru

We speak to the prominent AI researcher and leading critic of the AI industry, Timnit Gebru, who was recently awarded a 2026 Right Livelihood Award for her work investigating the harms and hype behind the global buildup of artificial intelligence. “Instead of discussing faulty, unreliable systems,” Gebru says tech leaders like SpaceXAI’s Elon Musk, OpenAI’s Sam Altman and Anthropic’s Dario Amodei are “marketing their systems as all-knowing, superpowerful, superintelligent,” and lobbying for “self-policing and self-regulation” to avoid being held “liable through existing laws.” Gebru expresses skepticism over increasingly widespread claims that AI algorithms pose an existential risk to humanity, warning that such claims are overblown and that “what is actually happening is regulatory capture.”

No shortcuts to research information citizenship - Digital Science

Being open isn't enough - true "research information citizenship" requires a robust, genuinely open research infrastructure.

AI Scientists as Engines of Discovery: A Case for Development within Reformed Institutions

Agentic artificial intelligence (AI) systems are beginning to assist, accelerate, and partially automate scientific discovery, performing tasks that span literature synthesis, code generation, data analysis, hypothesis proposal, and model criticism. We argue that this transition is qualitative rather than incremental, and that suitably designed multi-agent systems may evolve from passive computational tools into ``AI scientists'' that can expand the hypothesis-generating and verification capacity of science. Such systems must be developed and deployed within a scientific ecosystem fit for purpose: institutions must be redesigned for verification, accountability, interpretability, and dual-use safety. We sketch how multi-agent architectures, illustrated by the prototype framework \textit{Denario}, accelerate the discovery cycle and traverse model spaces beyond human reach; examine what this implies for authorship, peer review, and the enduring role of human scientists; and close with recommendations for governing AI as an epistemic actor rather than a mere instrument.

Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident

We’re on a journey to advance and democratize artificial intelligence through open source and open science.