Preparing National Research Ecosystems for AI: Strategies and Progress

Abstract

The International Science Council (ISC) unveiled its new flagship report Preparing National Research Ecosystems for AI, which synthesises 26 country case studies (including eight newly added in 2024). The panel – comprising ISC staff and case‑study authors from Kenya, Singapore and India – walked the audience through the report’s methodology, highlighted recurring challenges (policy gaps, compute and data inequities, skills shortages, funding shortages, and sustainability concerns) and showcased good practices (regional compute sharing, AI‑for‑Science funding streams, centres of excellence, and open‑curriculum initiatives). The session concluded with a lively audience Q&A that probed the academia‑industry gap in India, patterns for Global‑South inclusiveness, and the ethical implications of AI‑generated scholarly content.

Detailed Summary

Speaker	Main Points
Vanessa McBride (moderator)	• Welcomed participants and introduced the launch of the ISC report. • Described the ISC as a global non‑profit with > 200 member organisations spanning natural and social sciences. • Explained that the report (Version 3) contains 26 country case studies, of which eight are new (Egypt, Fiji, Hungary, Kenya, Namibia, Romania, Rwanda, Singapore). • Mentioned a 45‑issue literature review (2023) that was distilled to roughly 10 core challenges for AI uptake in science. • Listed the ten issues, e.g., scientific integrity, explainability vs. black‑box models, reproducibility, AI‑informed research funding, open‑data vs. closed‑algorithm tensions, and infrastructure harmonisation. • Stressed the report’s relevance for policy makers, national AI strategists, and individual researchers.
Felix Dijkstal (ISC)	• Prompted the audience to scan the QR code or visit the URL for the full report. • Emphasised that the case‑study “footprint” illustrates geographic diversity and that many issues identified in the literature remain unsolved today.
Jibu Elias (Mozilla Foundation)	• Briefly reiterated the purpose of the “structural gateways” that can accelerate AI‑driven scientific progress. • Highlighted the ISC’s ability to draw on its extensive membership for cross‑disciplinary learning.

1.1. Core Findings Across the 26 Case Studies

• Policy Gap: Most nations possess a national AI strategy, yet few have specific guidance for the research ecosystem.
• Compute & Data Access: Access to GPU clusters or high‑performance compute is uneven, creating a major bottleneck for researchers. Some regions are forming shared‑resource consortia to mitigate the gap.
• Data Stewardship: Science systems focus on outputs (papers, patents) but under‑invest in data curation and stewardship, which is essential for AI‑driven discovery.
• Skills Shortage: There is a pervasive lack of AI‑skilled workforce, especially for maintaining and interpreting AI pipelines.
• Funding Realignment: Existing funding mechanisms risk over‑investing in trendy AI applications at the expense of foundational humanities and social‑science research.
• Environmental Sustainability: Concerns about AI’s carbon footprint appear across many studies, but knowledge about mitigation strategies is limited.

A visual “four‑strand” framework (Strategy, Infrastructure, Capacity & Skills, Funding) is used in the report to map the maturity of each country (dark blue = established, light blue = emerging, grey = aspirational).

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2. Country‑Specific Deep‑Dives

2.1. Kenya – Moses M Thiga

Theme	Summary
Governance Structure	Kenya created a State Department for Research, Science, Innovation and Technology (2023) that consolidates three bodies: the National Commission for Science, Technology and Innovation (policy/regulation), the Kenya National Innovation Agency (innovation), and the National Research Fund (funding).
AI Policy	No dedicated AI policy yet. An AI strategy (2023) exists, aspiring to shift from applied to more foundational AI research, but it remains a draft.
Funding Landscape	General research funding is inadequate; no ring‑fenced AI budget. The only AI‑specific call (2023) was for cultural‑heritage research, a one‑off. Most funding comes from multinationals (Google, Microsoft, IBM, Gates Foundation), which also attract talent away from academia (brain drain).
Talent & Education	Growing number of AI‑related degree programmes (BSc, MSc, PhD) and numerous bootcamps/hackathons. However, career pathways remain fragmented; academia competes with industry for AI talent.
Compute Access	IBM/Google/Microsoft run private data‑centres, but costs are prohibitive for most researchers. The Kenya Education Network plans to launch a national GPU cluster (first educational HPC resource) later this month. Until then, heavy workloads are run on South Africa’s Centre for High‑Performance Computing.
Key Challenges	- Absence of AI‑specific policy and dedicated funding. - Limited compute infrastructure. - Talent drain to multinational labs or overseas. - Disjointed capacity‑building efforts.

2.2. Singapore – Kelly (Speaker not on the formal list)

Theme	Summary
Strategic Position	Singapore ranks high on three of the four report strands (Strategy, Infrastructure, Funding) but still faces data‑access and talent‑shortage challenges.
AI Strategy & Governance	Early‑adopted national AI strategy (2019) that envisions a smart‑city ecosystem and integrates AI across government functions, including scientific research.
Funding for AI‑for‑Science	≈ USD 120 million dedicated to AI‑for‑Science programmes; multiple agencies (e.g., A*STAR, AI Singapore) coordinate funding and research.
Centres of Excellence	AI Singapore and C‑Lion (a home‑grown large language model) illustrate a local‑first approach that respects Singaporean values while fostering regional collaboration.
Regional Leadership	Singapore acts as a regional hub, supporting neighbouring economies (e.g., Fiji) through data‑sharing and capacity‑building initiatives.
Talent & Skills	Persistent shortage of AI specialists; extensive up‑skilling programmes (bootcamps, university curricula) aim to close the gap. Emphasis on explainability, bias mitigation, and ethical governance.
Key Challenges	- Limited talent pool relative to ambition. - Need for regional cooperation to sustain AI research capacity. - Balancing indigenous data/values with global AI trends.

2.3. India – Felix Dijkstal (report author) & “Cheapo”

Theme	Summary
AI‑Readiness Gap	While AI strategies are now commonplace (even in smaller nations like Namibia), research‑ecosystem readiness (universities, compute, data, governance) is often missing.
Strengths	1. DPI tradition – strong data‑policy infrastructure (e.g., UPI system). 2. Policy attention – recent governmental announcements on AI compute & data. 3. Talent pipeline – India consistently ranks among the top 5 globally for AI talent and graduates millions of CS graduates annually.
Centres of Excellence (COEs)	In 2024 India announced four AI‑related COEs (Health, Agriculture, Sustainable Cities, Education – the latter at IIT Madras). Three are already operational.
Funding Constraints	Despite talent, R&D expenditure as a share of GDP is low; budget allocations for AI remain modest compared with China or the US.
Compute Scarcity	Recent procurement of 14 000 GPUs by the India Mission; access is subsidised but uneven—elite institutes receive priority, many universities still lack sufficient resources.
Data Stewardship	India is “data‑rich, data‑poor” – massive government data collections exist but are poorly curated, often in non‑machine‑readable PDFs. Ongoing effort to clean, standardise, and open datasets for researchers.
Faculty Capacity	Faculty training gaps at many IITs/central universities hinder translation of talent into research output.
Mozilla Initiative	The Responsible Computing Challenge (3‑year Mozilla program) partnered with nine Indian universities (including IIT Guwahati, IIT Indore, etc.) to develop open‑AI curricula, faculty development, and community‑driven governance frameworks.
Key Challenges	- Low R&D budget and uneven compute allocation. - Fragmented data stewardship despite abundant raw data. - Faculty skill gaps limiting effective AI adoption. - Need to balance infrastructure investment with human‑capacity development.

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3. Audience Q&A – Themes & Highlights

Questioner	Issue Raised	Main Responses / Insights
Avis Bita (Data‑Science student, India)	How to bridge the academia‑industry gap in India?	– Emphasised the need for intentional investment in joint programmes (e.g., industry‑sponsored labs, sandboxes, consultancy‑based research centres). – Cited German university‑industry models (FAU + Siemens) and IIT Madras‑Google collaboration as emerging examples.
Unnamed participant (Global‑South focus)	Patterns that could improve inclusiveness for the Global South?	– Panel noted youth‑population bulges as a capacity‑building opportunity if skills training is scaled. – Stressed data sovereignty and local compute as critical for self‑reliance. – Mentioned regional cooperation (e.g., Singapore’s support for Fiji) as a model.
Karthik (VIT student, India)	Research on foundation models vs. reliance on Western models; how can Indian startups compete?	– Recognised home‑grown models (e.g., Sarvam) but warned that foundational model ownership alone won’t solve ecosystem issues. – Highlighted the “policy dichotomy”: incentives for innovation yet limited support for domestic AI firms, contrasting with generous subsidies given to Google/Microsoft data‑centres.
Audience member (Publishing ethics)	Impact of AI on scientific publishing, integrity, hallucinations?	– Springer editor explained new AI‑use policy: AI can assist (language polishing, data analysis) if disclosed, but full‑text generation without oversight is prohibited. – Noted a spike in AI‑generated submissions post‑ChatGPT, with occasional hallucinated references. – Mozilla’s submission guidelines now require authors to declare AI assistance and share prompts where relevant.
Various participants	Philosophical concerns: cognitive atrophy, AI‑enabled humans vs. human‑enabled AI.	– Panel agreed AI can cause “cognitive debt” if over‑relied upon, underscoring the need for critical evaluation skills and human‑centric verification. – Suggested re‑thinking publish‑or‑perish pressure to reduce incentives for low‑quality, AI‑generated papers.
Open floor	General reflections on future frontiers for the Global South.	– Consensus that national AI policies must be concretised into research‑system road‑maps, with regional compute clusters, open‑data portals, and targeted up‑skilling programmes. – Emphasis on inclusive governance, ensuring local values shape AI development (e.g., Singapore’s C‑Lion model).

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4. Concluding Remarks

• Vanessa thanked the panelists and the audience, underscoring the importance of continuous dialogue between policy makers, researchers, and civil‑society actors.
• The session closed with a round of applause and an invitation for participants to explore the full report, download the QR‑linked PDF, and continue the conversation in upcoming ISC working groups.

Key Takeaways

1. Policy‑Implementation Gap – Most nations have AI strategies but lack specific, actionable guidance for research ecosystems, hindering coordinated AI adoption in science.
2. Compute & Data Inequity – Access to high‑performance compute and well‑curated, open data remains highly uneven, especially in low‑ and middle‑income countries; regional shared‑resource models (e.g., Kenya’s upcoming GPU cluster, Singapore’s regional hub) are promising mitigations.
3. Talent Drain & Skills Shortage – Strong AI talent pools exist (India, Kenya) but brain‑drain to multinational labs and insufficient up‑skilling programmes limit national capacity.
4. Funding Misalignment – Current research funding often favours trendy AI applications, risking under‑investment in foundational humanities and social‑science research.
5. Data Stewardship Deficit – While many countries are “data‑rich,” they lack systematic data curation, metadata standards, and open‑access pipelines, which are essential for trustworthy AI.
6. Centres of Excellence (COEs) as Catalysts – India’s health, agriculture, and sustainable‑city COEs, Singapore’s AI Singapore & C‑Lion initiatives, and Kenya’s nascent GPU cluster illustrate effective, focused investments that can be replicated elsewhere.
7. International Cooperation Is Crucial – Regional collaborations (e.g., Singapore supporting Fiji; African compute consortia) and global‑south networks can accelerate capacity building and safeguard data sovereignty.
8. Ethical Publishing Practices – Leading publishers now require explicit AI‑use disclosure; community‑driven guidelines (Mozilla’s prompt‑sharing policy) aim to preserve research integrity amid rising AI‑generated manuscripts.
9. Human‑Centred AI Education – To avoid “cognitive debt,” curricula must teach critical appraisal, explainability, and bias detection, ensuring researchers retain agency over AI outputs.
10. Strategic Recommendation – For equitable AI‑enabled science, nations should simultaneously invest in infrastructure, data stewardship, and human capital, while embedding inclusive governance that reflects local values and priorities.

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