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Joint-Meeting Session November 27, 2025

Ethical Agricultural Data and AI Governance

Plenary: RDA 26th Plenary Meeting (VP26)

Submitted by
Francis P. Crawley
Recording

Meeting objectives

Collaborative session notes:

Open session notes

Description

Agricultural data governance is undergoing rapid transformation driven by connected machinery, IoT devices, robotics, remote sensing, genomics, and the accelerating use of AI and machine learning. This session addresses the ethical, agricultural data and AI governance considerations around interoperability, sovereignty, and data visitation across agricultural data spaces. At the same time, global policy and regulatory frameworks (including the EU Data Act, the EU AI Act, emerging agricultural data spaces, updated Codes of Conduct, and UNESCO’s Open Science Toolkit instruments for crisis-data governance) are redefining rights, responsibilities, interoperability expectations, and equity considerations across food systems. These developments raise complex questions about how to align technological innovation with ethical and rights-based data stewardship in agriculture.

This joint session between the RDA Ethics in Agricultural Data Working Group (EAD-WG) and the EOSC-Future/RDA Artificial Intelligence and Data Visitation Working Group (AIDV-WG) brings together experts in data governance, AI governance, federated analytics, and data visitation to examine these challenges in an integrated way. Drawing on the EAD-WG Draft Recommendations for Agricultural Ethics 2025 and the AIDV-WG outputs on AI lifecycle governance, informed consent, secure processing environments, and federated/DV architectures, the session will explore how agricultural data systems, AI models, and research infrastructures can be governed as a coherent continuum rather than as isolated domains. It will make reference to the RDA Report on ‘Global Community Priorities for Agentic AI in Research: Consultation Results Available’.

The session will highlight key areas of convergence between the two Working Groups, including long-term data stewardship, foresight, crisis-data activation, and sovereignty–open science alignment. It will also identify gaps that require joint action and outline a shared roadmap for globally relevant, sovereignty-respecting, and open-science-aligned agricultural data and AI governance. The aim is to support interoperable, trustworthy, and equitable governance models that can guide agricultural digitalisation in both high-income and low- and middle-income contexts.

Principal Objective

To establish a coherent, ethically grounded, and interoperable governance framework that aligns agricultural data governance with AI governance by integrating FAIR, CARE, TRUST, ENVISAGE, and PILOT principles and enabling responsible use of data and AI across agricultural systems.

Supporting Objectives

Present and refine the EAD-WG Draft Recommendations 2025, including provisions on agricultural data spaces, long-term stewardship, sovereignty – open science alignment, and crisis-data governance, ensuring they support integrated agricultural data and AI governance.

Operationalise AIDV-WG tools and architectures (secure processing environments, data visitation workflows, informed consent guidance, legal foundations, AI Bill of Rights, and federated learning models) for practical application in agricultural contexts.

Evaluate the cross-sector applicability of emerging regulatory and policy frameworks, including the EU Data Act, EU AI Act, agricultural Codes of Conduct, and competition-policy guidance, to agricultural data, platforms, and AI-enabled services.

Develop a shared conceptual and technical understanding of how sovereignty, open science, and rights-based governance can be reconciled, particularly through the use of SPEs, DV frameworks, and other privacy- and sovereignty-preserving infrastructures.

Co-design a joint roadmap for future work, including harmonisation pathways for agricultural data spaces, SPE networks, and federated infrastructures, and the potential development of a shared Agricultural Data Visitation (DV) Reference Architecture.

Meeting presenters

Francis P. Crawley, Alan Moss, Ugochi Okengwu

Meeting agenda

90-minute agenda

Time Session Component Description / Topics Leads
00:00 – 00:05 Welcome
framing		 Opening remarks

Overview of session goals

Joint WG contexts

 EAD-WG Co-Chairs

AIDV-WG Co-Chairs
00:05 – 00:20 Presentation
The EAD-WG Draft Recommendations 2025		 Background and objectives of the EAD Recommendations

Target audience and drafting procedures

Ethical foundations (FAIR-CARE-TRUST-ENVISAGE-PILOT)

EU Data Act and agricultural data spaces; contractual fairness

UNESCO crisis-data governance; long-term stewardship and foresight

 EAD-WG presenting team
00:20 – 00:35 AIDV-WG: AI, DV, SPEs
consent, and legal frameworks		 Data visitation vs. federated learning

SPE operational design; AI Bill of Rights and risk classification

Consent models for AI and DV

 AIDV-WG presenting team
00:35 – 00:55 Joint panel
Convergence challenges and opportunities		 AI lifecycle alignment with agricultural governance; long-term curation of algorithms and models

Sovereignty vs open science using DV/SPEs

Cross-border and crisis-mode governance

Interoperability across agricultural data spaces

 Facilitated joint panel
00:55 – 01:20 Breakout groups
Joint roadmap development		 Objectives

  1. To develop an integrated roadmap that aligns agricultural data governance with AI governance
  2. To translate the combined outputs of the EAD-WG and AIDV-WG into practical, operational models
  3. To identify cross-cutting dependencies, convergence points, and critical gaps
 Co-Chairs
Group A
Technical architecture (SPE, DV, federated analytics)		 Identify requirements for interoperable technical components supporting secure processing environments, data visitation workflows, and federated analytics.

Develop a shared understanding of how SPE/DV infrastructures can enable sovereignty-preserving and rights-respecting data access across agricultural data spaces.

Outline technical priorities for an Agricultural Data Visitation Reference Architecture, including auditability, metadata continuity, and long-term model stewardship.

 Facilitator
Group B
Governance and ethics (rights, contracts, sovereignty)		 Examine governance frameworks that integrate FAIR, CARE, TRUST, ENVISAGE, and PILOT principles within agricultural data and AI systems.

Assess how rights, contractual fairness, and collective sovereignty can be operationalised in agricultural data spaces and cross-border data flows.

Propose governance mechanisms and oversight structures for SPE/DV deployments, crisis-data activation, and long-term data stewardship.

 Facilitator
Group C
Implementation in national and regional data spaces		 Review how national or regional agricultural data spaces can adopt interoperable governance, technical standards, and SPE/DV infrastructures.

Identify policy and institutional barriers to implementation, including regulatory alignment with the EU Data Act, AI Act, and Codes of Conduct.

Define practical next steps for piloting joint approaches in real-world data spaces, including capacity-building, coordination, and phased deployment.

 Facilitator
Group D
Crisis-data activation and long-term stewardship		 Define protocols for activating crisis-data workflows that balance timeliness with ethical safeguards, ensuring alignment with the UNESCO Open Science Toolkit instruments for data policies in times of crisis.

Identify long-term stewardship requirements for datasets and AI models used during crises, including metadata continuity, provenance tracking, risk mitigation, and future interpretability.

Propose governance mechanisms that ensure crisis-time data access does not become permanent or coercive, embedding sunset clauses, post-crisis review, community oversight, and intergenerational stewardship responsibilities.

 Facilitator
01:20 – 01:30 Plenary synthesis
Consolidation and next steps		 Summary of group outputs

Joint deliverables and milestones

Agreement on steps toward RDA Plenary 27

 Session
Co-Chairs

Target audience

The session is intended for a broad community of stakeholders engaged in agricultural data, AI, and cross-sector digital governance. These include agricultural data holders, farmers’ organisations, and Indigenous data-governance bodies, alongside AI developers, agritech companies, and robotics firms working at the interface of automation and data-driven agriculture. It also targets research infrastructures such as EOSC, AOSP, AgridataSpace, and CGIAR platforms, as well as policymakers, regulators, and public authorities shaping national and regional data strategies. The session will be of particular interest to RDA communities focused on semantics, repositories, legal interoperability, ethics, and domain-specific data infrastructures. Finally, international organisations (including FAO, CGIAR, GFAR, OECD, and AUDA-NEPAD) are key participants, given their roles in setting global norms and supporting capacity-building across food systems.

Group Activities and Scope

The RDA Ethics in Agricultural Data Working Group (EAD-WG) addresses the ethical, legal, and social dimensions of agricultural data across global food systems. Its work responds to rapid digitalisation in agriculture, including the expansion of sensor networks, robotics, satellite and drone monitoring, farm-management platforms, and AI-enabled analytics. The group has developed a comprehensive set of ethical recommendations centred on FAIR, CARE, and TRUST principles, while integrating frameworks such as ENVISAGE and PILOT for AI governance. Its scope encompasses agricultural data spaces, data rights, contract fairness, interoperability, crisis-data governance, long-term stewardship, and sovereignty–open science alignment. The EAD-WG’s outputs articulate how agricultural data ecosystems can be designed to protect farmers, Indigenous communities, and rural stakeholders while enabling equitable innovation.

The EOSC-Future/RDA Artificial Intelligence and Data Visitation Working Group (AIDV-WG) focuses on the governance, ethics, and technical frameworks required for responsible AI and advanced data-access models within open science. It develops guidance for ethics committees, informed consent in AI contexts, secure processing environments, federated analytics, and data visitation architectures, enabling high-value analysis without centralising sensitive data. The AIDV-WG also provides legal and operational foundations for AI lifecycle governance, model transparency, contestability, and risk management, including alignment with emerging regulatory frameworks such as the EU Artificial Intelligence Act. Its work supports data-intensive research across domains where sovereignty, privacy, and equity requirements are paramount.

Together, EAD-WG and AIDV-WG bring complementary expertise that spans the continuum from data governance to AI governance. Their collaboration enables the development of interoperable, sovereignty-respecting, and open-science-aligned frameworks for agricultural data and AI, especially in contexts where power asymmetries, sensitivity of geospatial and genomic data, and multi-jurisdictional flows demand advanced governance solutions. The joint work of these two RDA groups strengthens global capacity to implement trustworthy agricultural data spaces, secure processing environments, and federated or DV-based AI systems that uphold rights, support innovation, and ensure long-term stewardship across research and food-system ecosystems.

Additional links to informative material

  1. ALLEA. (2023). The European Code of Conduct for Research Integrity (Revised edition). https://allea.org/code-of-conduct
  2. AFA. (2023). Call to action from small-scale farmers on digital agriculture. /mnt/data/AFA – Call to action from smll-scale farmers on DA.pdf
  3. AIDV-WG – RDA Artificial Intelligence and Data Visitation Working Group. (2024). Guidance for ethics committees reviewing artificial intelligence and data visitation. https://www.rd-alliance.org/groups/artificial-intelligence-and-data-visitation-aidv-wg/outputs?output=165867
  4. AIDV-WG – RDA Artificial Intelligence and Data Visitation Working Group. (2024). Guidance for informed consent in the context of artificial intelligence and data visitation. https://www.rd-alliance.org/groups/artificial-intelligence-and-data-visitation-aidv-wg/outputs?output=143365
  5. AIDV-WG – RDA Artificial Intelligence and Data Visitation Working Group. (2024). AI Bill of Rights recommendation. https://www.rd-alliance.org/groups/artificial-intelligence-and-data-visitation-aidv-wg/outputs?output=165995
  6. AIDV-WG – RDA Artificial Intelligence and Data Visitation Working Group. (2025). Secure processing environments for open science: Proposing legal foundations. https://www.rd-alliance.org/groups/artificial-intelligence-and-data-visitation-aidv-wg/outputs?output=175108
  7. AIDV-WG – RDA Artificial Intelligence and Data Visitation Working Group. (2025). Shared citation library. https://www.rd-alliance.org/groups/artificial-intelligence-and-data-visitation-aidv-wg/outputs?output=165997
  8. Buendia, P., Kim, S., Meyers, N., Crawley, F. P., Farrell, G., Purian, R., & RDA AIDV-WG. (2025). Geographies of trust: AI, biomedicine, and the next era of federated and visiting data models (Version 1.0). Zenodo. https://doi.org/10.5281/zenodo.16929066
  9. CEADS. (2024). Common European Agricultural Data Space work programme (Internal working papers). European Commission.
  10. Copa-Cogeca, CEMA, CEETTAR, EFFAB, ESA, & Fertilizers Europe. (2020). EU Code of Conduct on agricultural data sharing by contractual agreement.
    /mnt/data/EU_Code_of_conduct_on_agricultural_data_sharing_by_contractual_agreement_2020_ENGLISH.pdf
  11. Crawley, F. P., Kim, S., Meyers, N., Purian, R., Farrell, G., & AIDV-WG. (2025). ENVISAGE and PILOT principles for AI governance (Framing paper). /mnt/data/ENVISAGE Principles & PILOT Principles Framing Paper 25-09-10_12.0 oa.pdf
  12. DigitalCouncil. (2024). Commentary for La Via Campesina on digital agriculture and platform power. /mnt/data/DigitalCouncilcommentSCholaCampesina.pdf
  13. European Commission. (2019). A smart and sustainable digital future for European agriculture and rural areas (DD3 Declaration). /mnt/data/EU-declaration-DD3Declarationonagricultureandruralareas-signedpdf.pdf
  14. European Commission. (2021). A European strategy for data. https://data.europa.eu/strategy
  15. European Commission. (2023). Revised EU Code of Conduct for agricultural data sharing. /mnt/data/European-Code-of-Conduct-Revised-Edition-2023.pdf
  16. European Commission. (2024). AgriDataSpace project description. https://agridataspace-csa.eu
  17. European Commission. (2025a). Guidance on the EU Data Act implementation for connected products and related services. Publications Office of the European Union.
  18. European Commission. (2025b). Guidelines on the implementation of the EU Artificial Intelligence Act. Publications Office of the European Union.
  19. European Commission. (2025c). Data economy and interoperability standards for EU data spaces. Publications Office of the European Union.
  20. European Data Protection Supervisor. (2014). Privacy and competitiveness in the age of big data. /mnt/data/EDPS-14-03-26_competitition_law_big_data_en.pdf
  21. European Parliamentary Research Service. (2016a). Precision agriculture and the future of farming in Europe. /mnt/data/future-digital-ag-EPRS_STU(2016)581892.pdf
  22. European Parliamentary Research Service. (2016b). Exploratory scenarios for precision agriculture. /mnt/data/future-digital-ag-exploratory-scenarios-EPRS_STU(2016)581892(ANN02)_EN.pdf
  23. European Parliamentary Research Service. (2016c). Horizon scan of precision agriculture. /mnt/data/future-digital-ag-horizon-scan-EPRS_STU(2016)581892(ANN)_EN.pdf
  24. FAO. (2021). Farm data sharing: FAO MOOC textbook. /mnt/data/FAO-MOOC-book-farm-data-sharing.pdf
  25. GFAR. (2018). Farmer-centred data governance: Brief 1. /mnt/data/GODAN-GFAR Brief 1 on farmers data.pdf
  26. GFAR. (2018). Farmer-centred data governance: Brief 2. /mnt/data/GODAN-GFAR Brief 2 on farmers data.pdf
  27. Global Indigenous Data Alliance. (2019). CARE principles for Indigenous data governance. /mnt/data/CARE+Principles_One+Pagers+FINAL_Oct_17_2019.pdf
  28. GODAN. (2018a). Ownership of open data in agriculture.
    /mnt/data/Godan_Ownership_of_Open_Data_Publication_lowres.pdf
  29. GODAN. (2018b). Responsible data in agriculture. /mnt/data/Godan_Responsible_Data_in_Agriculture_Publication_lowres.pdf
  30. Kaggle. (2024). Prototype to production: A guide to machine-learning system evaluation. /mnt/data/Prototype_to_Production-Kaggle.pdf
  31. Lin, D., et al. (2020). The TRUST principles for digital repositories. Scientific Data, 7, 144.
  32. Locus Charter. (2021). The Locus Charter for geospatial ethics. /mnt/data/Locus_Charter_March 2021.pdf
  33. Nairobi Declaration. (2023). Declaration on digital sovereignty and sustainable development. /mnt/data/Nairobi Declaration.pdf
  34. Purian, R., Crawley, F. P., Farrell, G., & AIDV-WG. (2024). Unlocking biomedical data potential: Data Visitation for RDA (DV4RDA). /mnt/data/Unlocking Biomedical Data Potential.pdf
  35. Rose, D. C., Lyons, K., & Chivers, C. (2021). Responsible development of autonomous robotics in agriculture. /mnt/data/Rose-et-al-Responsible development of autonomous robotics in agriculture-diagram-social-aspects.pdf
  36. Ryan, P. (2024). Stakeholder insights on the EU agricultural data sharing Code of Conduct. /mnt/data/Stakeholders Insights on the EU Agri Code of Conduct 2025.pdf
  37. UN. (2014). A world that counts: Mobilising the data revolution for sustainable development. https://www.un.org/en/datarevolution
  38. UN. (2021). UN human rights guidance note on data protection and data use. https://www.ohchr.org/en/resources/data-guidance
  39. UN. (2023). Governing AI for humanity: Interim report. https://www.un.org/en/ai-advisory-body
  40. UNESCO. (2025a). Developing data policies for times of crisis facilitated by open science: Factsheet. https://unesdoc.unesco.org/ark:/48223/pf0000393829
  41. UNESCO. (2025b). Developing data policies for times of crisis facilitated by open science: Guidance. https://unesdoc.unesco.org/ark:/48223/pf0000393830
  42. UNESCO. (2025c). Developing data policies for times of crisis facilitated by open science: Checklist. https://unesdoc.unesco.org/ark:/48223/pf0000393831
  43. Wilkinson, M., et al. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3, 160018.
  44. Wright, H., Chue Hong, N., & Jomier, J. (2024). FAIR and TRUST-worthy genomic reference governance. /mnt/data/Wright et al 2024 – FAIR Header Reference genome – a TRUSTworthy.pdf
  45. WUR, DAH, GFAR, & Agroecology Coalition. (2023). Digitalisation and agroecology conversation summary. /mnt/data/WUR-DAH-GFAR-AgroecologyCoalition-digitalization&agroecology-econversation-summary.pdf

Short Group Status

The RDA Ethics in Agricultural Data Working Group (EAD-WG) was established in 2023 to address the ethical, legal, and social dimensions of the rapidly expanding agricultural data ecosystem. It focuses on issues arising from digitalisation across the sector, including the use of connected machinery, IoT systems, remote sensing, robotics, genomics, and AI-driven analytics. The group is now in the final phase of its Working Group cycle, with its primary output, the Draft Recommendations for Agricultural Ethics in Agricultural Data 2025, being prepared for submission as an RDA Recommendation. At the time of P26, the EAD-WG is completing community review, refining implementation guidance, and finalising deliverables that articulate how FAIR, CARE, TRUST, ENVISAGE and PILOT principles can be operationalised within agricultural data spaces, long-term stewardship, crisis-data governance, and sovereignty – open science alignment.

The EOSC-Future/RDA Artificial Intelligence and Data Visitation Working Group (AIDV-WG) was launched in 2022 as part of the EOSC-Future initiative to develop governance, ethical, legal, and technical foundations for responsible AI and advanced data-access models in open science. The group is in an active deliverables phase, having already produced guidance for ethics committees, informed consent in AI contexts, the AI Bill of Rights, legal frameworks for Secure Processing Environments, and practical models for data visitation and federated analytics. It is currently finalising additional outputs on cross-domain governance, AI lifecycle risk management, and SPE/DV operationalisation that are highly relevant to data-intensive domains such as agriculture. The WG will continue into 2025 with a focus on implementation pilots and cross-RDA harmonisation.

Together, the EAD-WG and AIDV-WG offer complementary and highly synergistic expertise at a pivotal moment for agricultural and AI data governance. Their joint activities at the time of P26 include alignment of the EAD-WG Recommendations with AIDV-WG architecture (DV/SPE frameworks), collaborative work on interoperability for agricultural data spaces, exploration of rights-based and sovereignty-preserving models for AI and statistical workflows, and scoping of a shared Agricultural Data Visitation Reference Architecture. This joint session therefore represents both the consolidation of near-final EAD-WG outputs and the forward-looking integration of AIDV-WG governance tools, making the session timely, impactful, and directly supportive of RDA’s mission.

Applicable Pathways

FAIR, CARE, TRUST - Evaluation and Policy
FAIR, CARE, TRUST - Adoption, Implementation, and Deployment
Data Infrastructures and Environments - Generalist

Avoid conflict with the following groups

Sharing Rewards and Credit (SHARC) IG

What potential collaborations or synergies do you see between your Group/Birds of a Feather session topic and other RDA Groups or external organisations?

Complex Citation Implementation Interest Group (CCI IG)
Active Data Management Plans IG
Digital Twins IG
International Science Council (ISC), UNESCO, Global Open Science Cloud (GOSC), African Open Science Cloud (AOSC), T2P Center for Health Ethics Training & Consultancy VizAfrica, CODATA, Australian Research Data Commons (ARDC); UNESCO-CODATA project on 'Developing Data Policies for Times of Crisis Facilitated by Open Science (DPTC)'

Please indicate at least (3) three breakout slots that would suit your meeting.

Breakout 1. Monday, 16 March, 13:30-15:00 UTC
Breakout 4. Tuesday, 17 March, 13:30-15:00 UTC
Breakout 7. Wednesday, 18 March, 13:30-15:00 UTC