CONFEDERATION OF NGOS OF RURAL INDIA

The global dialogue on Artificial Intelligence at the United Nations marked a decisive shift toward practical, ground-driven innovation. It emphasized that AI must be inclusive, accessible, and responsive to real-world challenges—climate variability, resource stress, and market uncertainty. A key success was the alignment among policymakers, researchers, and technology leaders around a shared vision: AI as a tool not just for efficiency, but for reducing structural inequalities. By enabling access to real-time information, predictive insights, and transparent systems, AI can help level the playing field between large and small stakeholders, bridging gaps across regions and economies. The dialogue also highlighted the importance of data ownership, digital trust, and equitable access—ensuring that the benefits of AI are widely shared rather than concentrated. Overall, it set a clear global direction: AI must complement human judgment, expand opportunity, and act as a catalyst for more resilient, inclusive, and equitable systems worldwide.

• Safe, secure and trustworthy AI
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Interoperability of governance approaches
• Transparency, accountability, and human oversight

Amid changing geo-economic dynamics and rising inequality, AI offers a powerful pathway to bridge gaps-if governed wisely. AI capacity-building: Builds skills and infrastructure so all countries can participate and benefit, not just a few. Social, economic, ethical, cultural, linguistic, technical implications: Ensures AI reduces bias and inequality rather than deepening them across societies. Interoperability of governance approaches: Aligns global rules so AI can scale safely across borders without fragmentation. Transparency, accountability, and human oversight: Keeps AI decisions understandable, responsible, and ultimately under human control.

To effectively address these challenges, there is a need for global, interoperable data-sharing frameworks backed by strong privacy safeguards, along with real-time cross-border intelligence systems to detect and respond to trafficking and other social harms. AI must be designed with local-language and context-aware capabilities tailored to Global South realities, supported by robust public-interest data infrastructure across health, agriculture, mobility, and finance. This should be reinforced by ethical guardrails with enforceable accountability, including bias audits and human oversight, as well as dedicated funding that prioritizes social-impact AI over purely commercial innovation. Building capacity in developing regions through skills, institutions, and digital access is essential, alongside strong coordination between governments, private sector, and civil society for effective implementation. Crucially, AI systems must adopt a survivor-centered approach with built-in protection mechanisms, guided by clear global standards led by institutions such as the United Nations and UNESCO, with a focused commitment to reducing inequality and addressing vulnerability.

Amid shifting geo-economic currents and deep inequality, AI can help India close gaps—especially in agriculture and rural systems—if governed with a cooperative-first lens. Extend AI skills, tools, and infrastructure to farmers, FPOs, and cooperatives. This enables better crop planning, input use, credit access, and market intelligence at the grassroots. India is more diverse than any other nation Socially, economically , ethically , culturally , linguistic wise. Design AI for India's diversity—local languages, smallholder realities, and informal markets—so it reduces bias, protects livelihoods, and strengthens inclusive participation of cooperatives. Interoperability of governance approaches Standardized, interoperable data lets farm records, logistics, finance, and market platforms connect across states and borders—critical for scaling cooperative networks and exports. Transparency, accountability, and human oversight AI used in credit, insurance, procurement, and pricing must be explainable and contestable, with clear grievance systems and human-in-the-loop decisions for farmers and cooperatives. Traceability (farm-to-fork backbone) AI-enabled traceability verifies origin, quality, and sustainability, reduces fraud, and improves price discovery—especially when cooperatives can collectively manage trusted data. Cooperative commodities exchange market AI can power cooperative-led exchanges with real-time price discovery, demand forecasting, and quality grading. This reduces middlemen asymmetry, improves bargaining power, and ensures fair, transparent pricing for members. Cooperative export zones AI-driven quality control, compliance mapping, and logistics optimization can help cooperatives meet global standards, cluster production, and access higher-value export markets with lower transaction costs. Cooperative commodity grids Digital grids linking production, storage, transport, and markets—optimized by AI—enable efficient aggregation, reduce post-harvest losses, balance supply-demand, and stabilize prices across regions. Aligning AI governance with cooperative markets, export zones, and commodity grids can transform Indian agriculture into a transparent, traceable, and farmer-centric economic system that narrows inequality while scaling globally.It can make value chain more farmer centric and Administratively implementable.

A United Nations–led AI dialogue can align countries around shared rules, reducing fragmented regulations and building trust for cross-border AI systems. Platforms like the UN AI Advisory Body ensure that both advanced and developing nations shape standards together, making governance more inclusive. It also enables coordinated action on global risks—such as misinformation, cyber threats, and human trafficking—while promoting development-focused AI in areas like agriculture, health, and education. Overall, it turns AI governance into a cooperative global framework that balances innovation with equity and accountability.

AI Dialogue should build upon and actively connect cooperative-driven economic systems with existing global AI governance frameworks. Foundational anchors include the United Nations digital cooperation agenda, the UNESCO AI Ethics Recommendation, and the OECD AI Principles, alongside implementation platforms such as the Global Partnership on AI and the International Telecommunication Union AI for Good initiative. These must be meaningfully linked with cooperative institutions, producer organizations, and community-owned data ecosystems across the Global South. For the cooperative economy, the key gap is not the absence of principles but the absence of structured integration of grassroots economic systems—farmers, cooperatives, and informal producers—into AI governance and value chains. The AI Dialogue can add value by creating a bridge between global frameworks and cooperative infrastructures such as cooperative commodity exchanges, traceability systems, cooperative export zones, and digital commodity grids. This would ensure that AI-driven efficiencies in markets, logistics, and finance are equitably distributed rather than concentrated. Critically, the Dialogue can position cooperatives as custodians of trusted data—enabling federated data-sharing models that protect ownership while unlocking value. It can facilitate interoperable standards that allow small producers to participate in global markets, strengthen traceability for commodities, and reduce asymmetries in pricing and access. Beyond alignment, the AI Dialogue should enable implementation: mobilizing financing, building capacity at the cooperative level, and supporting open, inclusive digital public infrastructure. Its added value lies in transforming AI governance from a top-down regulatory exercise into a participatory economic architecture—where cooperatives become central actors in reducing inequality, strengthening resilience, and ensuring that AI serves collective prosperity rather than narrow interests.

AI Dialogue must be designed as a multi-stakeholder, action-oriented platform where each actor contributes according to its strengths while aligning with equity, inclusivity, and cooperative principles. Governments should provide enabling policy frameworks, ensure regulatory clarity, and integrate cooperative and community-driven AI models into national strategies—especially for sectors like agriculture, informal labor, and rural economies. Multilateral institutions can anchor global norms, facilitate knowledge-sharing across regions, and ensure that Global South priorities—such as livelihood security, traceability, and fair market access—are embedded in AI governance. Private sector actors, particularly technology firms, should contribute interoperable, open, and ethical AI solutions while committing to transparency and data responsibility. Startups and innovators can pilot localized AI applications—such as cooperative commodity exchanges, decentralized traceability systems, and farmer-centric advisory tools—that demonstrate scalable impact. Academia and research institutions should generate evidence-based insights, develop inclusive datasets, and support capacity-building for underserved communities. Critically, cooperatives, farmer producer organizations, and civil society must be positioned not as beneficiaries but as co-creators—bringing grassroots intelligence, contextual knowledge, and trust-based networks into AI design and deployment. In terms of format and structure, the AI Dialogue should adopt a tiered and iterative model: (1) Thematic working groups focused on key areas such as cooperative digital infrastructure, ethical AI, and inclusive data governance; (2) Regional consultations to capture diverse socio-economic realities; and (3) A global convening platform that synthesizes inputs into actionable policy roadmaps. A permanent knowledge and coordination hub should track commitments, share best practices, and enable continuous engagement. It may add value by ensuring that AI governance is anchored in cooperative economics—prioritizing shared ownership of data, equitable value distribution, and systems that reduce inequality while strengthening local and global economic resilience.

Voices of farmers and cooperative organisations—who anchor real-world production, sustainability, and local economies—remain significantly underrepresented in global AI governance discussions. This gap risks designing systems that optimise efficiency and profit while overlooking equity, traceability, and livelihood resilience. Farmers and cooperatives are not just "end users" of AI; they are data generators, custodians of biodiversity, and critical actors in value chains. Without their inclusion, AI frameworks may deepen existing inequalities, particularly across the Global South. To democratise value chains, their participation must move from symbolic consultation to structured representation. First, global AI dialogues should mandate dedicated seats for farmer organisations and cooperative federations, ensuring regional diversity and gender inclusion. Second, participatory policy design mechanisms—such as local-to-global consultation pipelines—should be institutionalised, where grassroots inputs are aggregated through cooperatives and fed into international forums. Third, digital public infrastructure must be leveraged to enable farmer data ownership through cooperative data trusts, ensuring fair value capture and consent-based data sharing. Capacity building is equally critical. Investments in AI literacy for cooperatives, along with support for community-led innovation labs, can empower farmers to co-create solutions tailored to local challenges such as climate risk, market access, and traceability. Partnerships with research institutions, startups, and multilateral agencies should prioritise co-design models rather than top-down deployment. Finally, governance frameworks must recognise cooperatives as economic institutions capable of managing AI-enabled platforms—such as cooperative commodity exchanges, traceability systems, and export networks—ensuring transparency and equitable distribution of benefits. Embedding farmers and cooperatives at the centre of AI governance is not only a matter of inclusion; it is essential for building resilient, fair, and accountable global value chains.

Engagements formats for an AI Dialogue must be simple, participatory, and grounded in real-life challenges rather than abstract policy discussions. First, "Field-to-Forum Dialogues" can connect farmers directly with policymakers and technologists through hybrid village-level meetings linked to global sessions. Using local languages and AI-enabled translation tools ensures inclusivity while bringing authentic grassroots insights into global governance spaces. Second, "Problem-to-Solution Hackathons" focused on real farmer challenges—such as price discovery, climate risks, traceability, and access to markets—can invite agri-startups, cooperatives, and AI developers to co-create solutions with farmers, not for them. This ensures relevance and adoption. Third, "Cooperative Data Labs" can be established where farmer producer organizations and cooperatives collectively own and manage their data. These labs can demonstrate how AI models can be trained on community-owned datasets, ensuring fair value distribution and data sovereignty. Fourth, "Immersive Demonstration Platforms"—including mobile-based simulations, voice bots, and offline-first AI tools—can allow farmers to अनुभव (experience) AI applications in crop planning, pest control, and logistics, making engagement practical rather than theoretical. Fifth, "Farmer Jury Panels" can be introduced as a governance innovation, where selected farmers evaluate AI use cases and provide structured feedback on ethics, accessibility, and impact, ensuring accountability. Finally, "Cooperative Exchange Showcases" can highlight AI-enabled commodity exchanges, export zones, and cooperative grids, demonstrating scalable models that reduce intermediaries and improve farmer incomes. Together, these formats shift the AI Dialogue from being technology-driven to farmer-led, ensuring that innovation aligns with equity, trust, and real economic empowerment.

An approach to AI governance must combine inclusion, accountability, and real value at the grassroots. The focus should shift from top-down technology deployment to systems where farmers have agency over data, decisions, and benefits. First, participatory data governance is essential. Models such as Digital Green show how farmers can co-create and validate AI-driven advisories using local knowledge. This ensures AI systems are context-specific, reduce bias, and build trust among farming communities. Second, strong public digital infrastructure plays a critical role. India Stack demonstrates how interoperable systems-covering identity, payments, and consent-based data sharing-can enable AI solutions for credit access, insurance, and market linkages while protecting farmer data rights. Third, farmer-owned digital platforms and cooperatives should be central to AI ecosystems. Policies can promote AI-enabled commodity exchanges, traceability systems, and cooperative data grids where farmers collectively negotiate prices, reduce dependency on intermediaries, and gain transparency across value chains. Fourth, adaptive regulatory frameworks are needed. NITI Aayog has emphasized responsible AI through principles like transparency, explainability, and fairness. Regulatory sandboxes allow safe experimentation of AI tools in agriculture while aligning them with local socio-economic realities. Fifth, open and interoperable data ecosystems are key to innovation. Policies should encourage anonymized, shared datasets on weather, soil, and markets to power AI-driven advisories and climate resilience tools, while ensuring farmers retain control over personal and farm-level data. Finally, capacity-building must be prioritized. AI governance should include large-scale digital literacy and extension efforts so farmers can understand, question, and effectively use AI systems rather than passively adopting them. Overall, effective AI governance for farmers lies in shifting power toward them-through data ownership, collective platforms, and inclusive policy design-so AI becomes a tool for reducing inequality and strengthening rural economies.