GEO Indigenous Allianec & Spaec4Innovation

A successful first Global Dialogue on AI Governance would move beyond broad principles and establish clear pathways for implementation, accountability, and inclusion. From the perspective of the GEO Indigenous Alliance and Space4Innovation, success would mean that Indigenous Peoples, local communities, and other historically excluded knowledge holders are recognized not as stakeholders at the margins, but as governance actors with expertise essential to the future of AI. The Dialogue should produce concrete commitments to inclusive governance, including mechanisms for meaningful participation, culturally grounded safeguards, and stronger recognition of Indigenous data sovereignty. It should also acknowledge that AI governance is not only about managing technical risk, but about addressing power, consent, benefit-sharing, and the conditions under which data are collected, interpreted, and used. A strong outcome would include attention to AI systems applied in environmental monitoring, biodiversity, language, and cultural knowledge, where communities are often affected long before governance frameworks are in place. The Dialogue should help shape governance that is relational, rights-based, and responsive to place. Finally, success would mean creating an ongoing process, not a one-off event: one that supports global coordination while respecting diverse governance systems, knowledge traditions, and legal realities. The first Dialogue should set a foundation for AI governance that is ethical, practical, and accountable to both people and the living world.

• Protection and promotion of human rights
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Safe, secure and trustworthy AI
• Transparency, accountability, and human oversight

Transparency, accountability, and human oversight are essential because many AI systems are already shaping decisions that affect lands, ecosystems, species, and communities without sufficient clarity around how data are used, who makes decisions, or who is responsible when harm occurs. Our recent work, including the GEO Indigenous Alliance's ethical toolkit on animals and AI, has shown that stronger governance is urgently needed when living beings become data. Protection and promotion of human rights is central because AI governance must safeguard collective rights, cultural rights, and the rights of Indigenous Peoples, including rights related to land, knowledge, consent, and data governance. We also prioritize the social, economic, ethical, cultural, linguistic and technical implications of AI because these systems do not operate in a vacuum. They shape whose knowledge counts, whose languages are supported, and who benefits or is excluded. Finally, AI capacity-building is critical. Communities must not only be consulted after systems are built; they must be enabled to shape design, governance, and use from the outset. Capacity-building should therefore include technical access, governance literacy, and support for Indigenous-led innovation.

Yes. One important cross-cutting issue is Indigenous data sovereignty and community governance over data, models, and downstream use. Current AI debates often focus on privacy or safety at the individual level, but many communities face collective risks that are not adequately addressed by existing governance language. This includes the extraction of ecological, linguistic, cultural, and territorial data without meaningful consent or fair benefit-sharing. A second emerging issue is AI's relationship with the more-than-human world. As AI is increasingly used in biodiversity monitoring, conservation, land management, and environmental forecasting, governance frameworks must consider not only human impacts but also the ethical implications of turning animals, ecosystems, and living relations into machine-readable datasets. The GEO Indigenous Alliance and Space4Innovation, are leading the first Indigenous-led ethical AI toolkit designed to guide policymakers, scientists, and practitioners working at the intersection of biodiversity, animal data, and AI. This work responds to a major governance gap: there are still very few practical frameworks to help institutions assess consent, responsibility, benefit-sharing, and potential harm when AI systems are applied to living systems. We believe this dimension must be recognized as a core part of future AI governance. A third issue is epistemic equity: the need to ensure that governance frameworks do not treat Indigenous knowledge as supplementary or symbolic, but as a valid and necessary source of insight for shaping responsible AI. Without this, AI governance risks reproducing existing inequalities under a new technical language. For these reasons, future dialogue should explicitly address collective rights, relational accountability, and governance models grounded in reciprocity, stewardship, and long-term responsibility.

Governance gaps in AI are already affecting our sector significantly, particularly in the areas of biodiversity monitoring, Earth observation, Indigenous knowledge, and environmental decision-making. Across these fields, AI systems are advancing faster than the governance frameworks needed to guide their responsible use. This creates serious challenges around accountability, consent, transparency, and benefit-sharing. One of the most significant challenges is that communities are often asked to contribute knowledge, environmental observations, or locally grounded data without having meaningful power over how these data are used, interpreted, or embedded into AI systems. In many cases, governance approaches remain overly technical and do not adequately address collective rights, Indigenous data sovereignty, or the ethical implications of applying AI to living systems such as animals, ecosystems, and culturally significant landscapes. Another challenge is uneven capacity. Many Indigenous communities and locally led organizations are expected to engage with AI-related processes without sufficient access to technical infrastructure, legal support, or governance tools. This risks deepening existing inequalities, even where AI is presented as innovative or beneficial. At the same time, there is a major opportunity to shape a different path. In our sector, AI can support biodiversity protection, strengthen community-led monitoring, improve environmental early warning, and help make complex data more accessible. There is also growing momentum for Indigenous-led governance models that bring relational accountability, stewardship, and long-term thinking into AI debates. This is precisely where our work is focused. Through the GEO Indigenous Alliance and Space4Innovation, including our Indigenous-led ethical AI toolkit for policymakers and scientists, we see an opportunity to help build governance frameworks that are more inclusive, practical, and responsive to both human communities and the more-than-human world.

The AI Dialogue can play an important role by creating a trusted multilateral space where governments, Indigenous Peoples, scientists, civil society, and technical actors can address governance challenges that no country can solve alone. The Global Digital Compact already recognizes the need for international cooperation on AI, and the Dialogue can help turn that commitment into a more practical and inclusive process. Its value lies not only in connecting governments, but also in connecting levels of governance that are often kept apart: global frameworks, national policy, sectoral practice, and community-led experience. This is where bodies such as the GEO Indigenous Alliance are especially relevant. Through its work bridging Indigenous Peoples, Earth observation, and the space sector, it shows that governance must be grounded not only in technical standards, but also in knowledge systems, stewardship responsibilities, and real-world implementation. The Dialogue can therefore help broaden who is recognized as a governance actor and ensure that international cooperation includes perspectives too often left outside formal AI discussions. It can also help surface emerging issues such as Indigenous data sovereignty, collective rights, and AI's relationship with the more-than-human world. In this way, the Dialogue can strengthen cooperation by making AI governance more coherent, more inclusive, and more responsive to the realities already unfolding across regions and sectors.

The Dialogue should connect with initiatives such as the GEO Indigenous Alliance, which was founded at the 2019 GEO Canberra Ministerial Summit and works to bridge Indigenous Peoples, Earth observation, and the broader GEO and space sectors. The GEO Indigenous Alliance brings practical experience in building governance approaches that respect Indigenous knowledge, cultural heritage, and more inclusive environmental decision-making. It should also engage implementation spaces where AI is already being applied in environmental monitoring, biodiversity, and Earth observation, including communities working through GEO and related data governance mechanisms such as GEOSS. These are areas where governance questions are no longer theoretical. The added value of the AI Dialogue would be to connect these efforts at a higher political level, while filling an important gap: many existing mechanisms still do not adequately include Indigenous Peoples, community governance models, collective rights, or the ethical implications of AI for the more-than-human world. The Dialogue can help bring these perspectives into mainstream international AI governance in a more coherent, practical, and action-oriented way.

For entities such as the GEO Indigenous Alliance and Space4Innovation, contribution is strongest where governance, environmental monitoring, Indigenous knowledge, and implementation meet. The Dialogue should therefore create space for sector-specific contributions, including biodiversity, Earth observation, and community data governance. It should also ensure interpretation, accessible formats, and support for participants who are often invited into global processes without the resources to engage fully. A successful structure is one that treats inclusion as part of governance design, not as an afterthought.

Global AI governance discussions still underrepresent Indigenous Peoples, local communities, Global South practitioners, frontline environmental actors, speakers of underrepresented languages, and those working at the intersection of AI with biodiversity, land, water, and cultural knowledge. Although the UN process emphasizes engagement with all relevant stakeholders, these perspectives remain far less visible in most formal AI discussions than government, industry, and large technical institutions. In our view, Indigenous Peoples are particularly underrepresented not only in terms of participation, but in terms of authority. Too often they are consulted as affected groups rather than recognized as governance actors with relevant systems of law, stewardship, and data governance. This is especially important where AI systems intersect with territories, ecosystems, languages, cultural heritage, and collective forms of knowledge. Inclusion requires more than invitations. It requires funded participation, early involvement in agenda-setting, multilingual access, and formats that allow oral, community-based, and practice-led contributions alongside written submissions. It also requires thematic space for issues often left at the margins, including Indigenous data sovereignty, collective rights, and AI's implications for the more-than-human world. The Dialogue should recognize that more representative governance is not only fairer; it also leads to better decisions.

One especially valuable format would be an Indigenous-led innovation hackathon methodology. This is not a conventional hackathon focused only on technical outputs. It is a participatory, Indigenous-led methodology that has been developed and tested internationally for over a decade to bring together Indigenous leaders, youth, scientists, policymakers, and technical experts to co-design responses to complex challenges. Through this approach, participants do not simply discuss innovation; they engage directly with questions of governance, responsibility, ethics, and long-term impact. This model has proven effective across diverse cultural and geographic contexts because it centers relationship-building, co-creation, and local leadership rather than extractive or top-down design. In the context of the AI Dialogue, such a format could help participants work through practical issues such as consent, data sovereignty, accountability, environmental monitoring, and the ethical use of AI in culturally and ecologically sensitive settings. It would also create a more dynamic and action-oriented space than traditional panel discussions alone.

One important example is the development of Indigenous-led ethical governance tools for AI. Through the GEO Indigenous Alliance and Space4Innovation, we are helping lead the development of one of the first Indigenous-led ethical AI toolkits for policymakers, scientists, and practitioners, focused on the use of AI in biodiversity, animal data, and environmental monitoring. This toolkit responds to a major gap in current governance approaches. Many existing AI frameworks remain broad and high-level, while practitioners in fields such as conservation, Earth observation, and ecological monitoring still lack practical guidance on how to address issues such as consent, accountability, Indigenous data sovereignty, benefit-sharing, cultural sensitivity, and potential harm when living beings and ecosystems are turned into machine-readable data. Our approach is designed to make these questions operational, not just aspirational. A second example is the use of Indigenous-led co-design methodologies, including the Indigenous innovation hackathon model developed and tested internationally over more than a decade through Space4Innovation. This approach has shown that effective governance is not only about regulation after technologies are built, but about shaping systems from the outset through inclusive design, local leadership, and long-term responsibility. A third promising approach is the combination of rights-based governance frameworks with sector-specific implementation tools. In our view, effective AI governance requires both: high-level principles such as transparency, accountability, and human rights, and practical mechanisms that can guide decision-making in real contexts. Together, these kinds of tools and approaches help move AI governance from abstract principle to applied practice, while ensuring that communities most affected by AI have a meaningful role in shaping how it is governed.