Science in the Age of AI: Knowledge, Data, and Trust
International Science Council & Committee on Data of the ISC
Session 216
Background
Artificial Intelligence is reshaping the scientific enterprise, but current governance discussions have paid insufficient attention to AI's systemic implications for science as a knowledge system. While social, economic, ethical, and technical dimensions of AI are being debated in multilateral forums, the impact on scientific knowledge production, validation, and sharing has been largely overlooked. This gap is particularly significant for science institutions and researchers in the Global South, who face distinct challenges: openly available scientific data generated in their regions is increasingly harvested to train commercial AI systems without reciprocal benefit or data governance frameworks that protect scientific sovereignty.
The International Science Council's Science Systems Futures programme and AI disclosure in research initiative, alongside CODATA's work on FAIR data and governance for AI, demonstrate that the international science community has begun to address these questions. However, these initiatives remain dispersed across institutional networks. The WSIS Forum provides a critical venue to surface this evidence base and connect it to broader digital governance conversations, ensuring that science's stakes in responsible AI development are visible to policymakers and multilateral processes.
Session themes
The session examines three interconnected dimensions: (1) AI and knowledge generation—how autonomous AI roles in research affect the scientific method and who counts as a knowledge producer; (2) Scientific data and AI models—the tension between open science principles and data sovereignty, especially for the Global South; and (3) Reliability and research integrity—what governance mechanisms are needed as AI-generated outputs enter the scientific record.
Relevant projects and practices
The session draws on evidence from multiple initiatives: ISC's work on a global AI disclosure standard for research (the Vancouver Standard); CODATA's Task Groups on data quality and governance for AI; and national examples of science systems navigating public-private partnerships around emerging technologies in the Global South. These initiatives provide practical grounding for discussions about what norms, standards, and frameworks are needed.
Vision for WSIS Beyond 2025
The WSIS commitment to science as a global public good (reflected in action line C7 on E-science) must evolve to address AI's role in determining what knowledge gets produced, who produces it, and who benefits from it. WSIS Beyond 2025 should position the science and research community as central stakeholders in AI governance, ensuring that discussions of data governance (GDC Objective 4) and responsible AI (GDC Objective 5) are grounded in the needs and equity concerns of the global scientific enterprise, particularly in the Global South.
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C2. Information and communication infrastructure
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C3. Access to information and knowledge
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C4. Capacity building
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C6. Enabling environment
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C7. ICT applications: benefits in all aspects of life — E-science
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C10. Ethical dimensions of the Information Society
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C11. International and regional cooperation
C2 (Information and communication infrastructure): AI governance requires understanding how data infrastructure—where it is stored, who controls it, how it flows—shapes scientific capacity in different regions.
C3 (Access to information and knowledge): Open science principles intersect with AI's use of scientific data; governance frameworks must balance openness with equitable control and benefit-sharing.
C4 (Capacity building): Science institutions require new skills and governance capacities to navigate AI's integration into research, particularly in the Global South.
C6 (Enabling environment): Policy frameworks are needed to support responsible AI use in research while protecting scientific integrity and data sovereignty.
C7 (ICT applications: E-science): This is the core action line; the session directly addresses how AI, as an emerging ICT, reshapes scientific discovery and research systems.
C10 (Ethical dimensions of the Information Society): AI in science raises fundamental questions about research ethics, accountability, and the public interest.
C11 (International and regional cooperation): Equitable AI governance for science requires multilateral cooperation and frameworks that serve all regions.
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Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all
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Goal 9: Build resilient infrastructure, promote sustainable industrialization and foster innovation
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Goal 16: Promote just, peaceful and inclusive societies
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Goal 17: Revitalize the global partnership for sustainable development
SDG 4 (Quality education): AI's role in knowledge production affects how science education and research training evolve.
SDG 9 (Industry, innovation, infrastructure): Science and technology innovation increasingly depends on AI; governance of this intersection shapes innovation pathways globally.
SDG 16 (Peace, justice, and strong institutions): Research integrity and trustworthy knowledge are foundations for evidence-based policymaking.
SDG 17 (Partnerships for the goals): Equitable public-private partnerships in science-technology sectors depend on governance frameworks that protect public interest and Global South equity.
- Objective 1: Close all digital divides and accelerate progress across the Sustainable Development Goals
- Objective 4: Advance responsible, equitable and interoperable data governance approaches
- Objective 5: Enhance international governance of artificial intelligence for the benefit of humanity
https://council.science/events/wsis-forum-2026/
https://council.science/our-work/science-systems-futures/