Unlocking Impact: a Shared Data Platform for AI Innovation in Development Cooperation

Abstract

The workshop opened with a brief agenda overview and a moderated panel that examined the evolving role of data and artificial intelligence (AI) in development cooperation. Panelists highlighted how data has shifted from a static service‑delivery input to a dynamic predictor of outcomes, especially in crisis contexts such as the COVID‑19 pandemic. Gaps identified include siloed data, insufficient community‑level participation, and limited capacity‑building structures. Governance challenges—data sovereignty, interoperability, and the speed mismatch between AI development and bureaucratic processes—were explored, together with practical principles for low‑ and medium‑maturity countries. The session then showcased a live demonstration of Nefele, a German‑government‑sponsored shared data platform that integrates open‑source AI models, vector databases, and an emerging “context layer.” The demo illustrated project‑level data extraction, geospatial visualisation, and conversational AI interaction. The session concluded with a vigorous Q&A that probed data‑access policies, private‑vs‑public repositories, sensitive‑data handling, and the future of AI‑driven job creation in developing economies.

Detailed Summary

• Moderator (Kirlin/Karo) introduced the session, outlined a 55‑minute schedule, and announced three parts:
  1. Panel discussion – exploring visions, governance, and practice of shared AI‑ready data infrastructures.
  2. Demo – a live walk‑through of the German‑government Nefele platform.
  3. Q&A – audience interaction.

2. Panel Discussion – Perspectives on Data & AI in Development Cooperation

2.1. The Evolving Role of Data (Navya Alam, UNDP)

• Historical trajectory – From 2010‑2020, data collection and digitisation were primarily used for service‑delivery dashboards.
• Pandemic catalyst – COVID‑19 forced real‑time analytics (e.g., vaccine distribution, disease‑spread modelling) and demonstrated the need for predictive, outcome‑oriented data.
• AI‑enabled pattern recognition – AI now extracts risk‑profiles (e.g., vulnerability of migrant groups, emergent surveillance) from administrative registries such as Aadhaar‑type social registries.

Key Insight: Merely having data is insufficient; the shift is toward real‑time, predictive analytics that inform adaptive interventions.

2.2. Gaps Between Promise and Impact (Gaurav Godhwani / “Nupura Gavde”)

• Data abundance vs. usability – Data is plentiful, but resides in institutional silos; inter‑departmental sharing is minimal.
• Participatory deficit – Current platforms emphasise top‑down reporting; community‑generated data (e.g., village elder disaster logs) remains untapped.
• Capacity‑building fragmentation – Governance training and community skill‑development are pursued separately, limiting co‑creation.

Recommendation: Foster participatory, co‑created data pipelines that bring community‑level observations into the AI workflow.

2.3. Government‑Centric Initiatives (Dr. Iliya Nickelt‑Czycykowski, BMZ)

• Funding & external motivation – EU sponsorship enabled a cross‑ministerial data‑lab within German ministries (≈ 3‑4 pilot platforms).
• Infrastructure landscape – Two platforms: a secure, intra‑government system (Foreign Office) and an open, collaborative system (intended for partners such as India). Both are cloud‑based.
• Procedural bottlenecks – Data exchange with partners (GIZ, KfW) is 10‑50× slower than AI model development cycles, highlighting a policy‑process vs. technology speed mismatch.

Key Insight: Technical agility must be matched with policy‑process reforms to realise AI‑driven solutions at governmental scale.

2.4. Governance Principles for Varied National Contexts (Navya Alam, UNDP)

1. Governance without formal law – In low‑maturity settings, soft‑governance logics (principles, norms) can substitute for absent legislation.
2. Use‑case first – Identify a clear public‑policy problem before drafting governance structures.
3. Distinguish technical vs. operational interoperability – Technical standards (APIs, data formats) must be complemented by operational agreements (roles, responsibilities, enforcement).
4. Sequencing over “one‑size‑fits‑all” – Gradual adoption of standards (e.g., GDPR‑style data‑protection) avoids premature, unsustainable imports.
5. Capacity‑building & contextualisation – Blend global best‑practices with local policy realities; invest in long‑term expertise rather than short‑term checklists.

2.5. International Collaboration & Sector‑Specific Opportunities (Gaurav Godhwani)

• Sectoral data collaboratives – Combine standards, analytical tools, evaluation frameworks, and raw datasets into a shared ecosystem (illustrated with climate‑action projects across India and the Asia‑Pacific).
• Open‑source foundation – Base layer must be open‑source to ensure accessibility and reproducibility; later layers may incorporate proprietary or sensitive data under controlled access.
• Celebrating “data changemakers” – Recognise practitioners who co‑create, contribute, and disseminate tools, fostering a self‑sustaining community that breaks silos.

2.6. Defining the First Question for Governments (Navya Alam & Gaurav Godhwani)

• Navya’s advice – Start with “What problem are we trying to solve? Who are the beneficiaries? How does technology serve that need?” Emphasised that technology is an enabler, not a cure.
• Gaurav’s response – Stress “co‑creation” – governments must partner with practitioners to design infrastructure that is both useful and maintainable.

2.7. Reflections on Data‑Sovereignty & Sensitivity (Audience Question & Panel Response)

• Panel consensus: Governance frameworks are still nascent; while the German data‑lab has internal rule‑books, a universal “data‑space” rule‑set is still being drafted.
• UNDP perspective – Adopt tiered classification (sensitive vs PII‑free) and allow sector‑specific rule‑books that do not block data contribution outright.

3. Live Demonstration – The Nefele Platform

3.1. Platform Philosophy

• Data‑plus‑Context – Not only raw data but also the semantic context (metadata, provenance, domain knowledge) is stored, enabling AI models to interpret data correctly.
• Agents & “Maltbook” – Introduced an AI‑agent exchange hub (named “Maltbook”) where autonomous agents can converse, share insights, and eventually interact with human users.

3.2. User Interface & Core Features (shown by Dr. Iliya)

1. Project‑overview page – Lists BMZ‑financed projects (e.g., a 25‑year‑old KfW‑funded renewable‑energy project in India).
   • Data extracted from PDF‑style project reports using LLM‑driven extraction, stored in a vector database rather than a relational DB.
2. Conversational AI layer – Users can chat with the extracted data:
   • Example query: “What were the five biggest outcomes of that project?” – AI returns a probabilistic answer (≈ 70‑80 % accuracy).
3. Geospatial visualisation – Demonstrated retrieval of GeoJSON location data, plotted on a map widget with interactive circles highlighting project sites.
4. Contextual reasoning – Showed how a retrieval‑augmented generation (RAG) pipeline can supply a model with project‑specific facts​, enabling richer dialogues.

3.3. Technical Highlights

• Open‑source stack – Vector database (likely FAISS or Qdrant) + LLM‑based extraction (e.g., GPT‑4).
• Layered security – Two‑tiered model: open‑data layer for demos, secure layer for sensitive datasets (access times 5‑10× slower).
• Agent ecosystem – Early vision to let software agents (not just humans) query the platform, requiring trust frameworks and governance policies for safe operation.

3.4. Vision Beyond the Demo

• Phase 1 – Demonstrate basic data‑to‑AI pipelines with open datasets.
• Phase 2 – Introduce AI‑agent collaboration (Maltbook) while maintaining human oversight.
• Phase 3 – Deploy a trustworthy, regulated environment where autonomous agents can negotiate data access, respecting sovereignty and privacy.

4. Audience Q&A – Highlights

Question	Main Points of Response
Is the platform publicly available?	Not yet; the demo used open data, but the full environment (including the “context‑registry” for agents) is still under development.
Can a private Git repository serve as the “central context”?	Panelists agreed that a private repository can host governance rules and shared assets, but scaling across multiple organisations requires formal governance models (access control, audit trails).
How are sensitive sources handled?	Currently, sensitive data lives in a secure tier with higher latency. The rule‑book for data‑spaces is still being drafted; the German labs are experimenting with layered access and redaction workflows.
What governance rules enable cross‑stakeholder cooperation?	No universal rule‑book yet; the German data‑lab aims to create a common governance template. UNDP emphasises sector‑specific guidelines (e.g., procurement data, climate‑adaptation data) that balance openness with privacy.
How can AI create jobs rather than displace them?	UNDP stressed long‑term, systematic capacity‑building (human‑in‑the‑loop, upskilling). CivicDataLab highlighted the need for co‑creation so that the technology is built with practitioners, ensuring sustainable roles.
What about interoperability between different national data spaces?	Panel reiterated the need for operational interoperability (shared governance processes) in addition to technical standards. Sequencing and incremental adoption are key.

5. Closing Remarks

• Moderator thanked panelists and the audience, emphasized the importance of trusted data‑AI infrastructure for impact in development cooperation, and invited attendees to continue discussions after the session.
• Brief photo‑op and moment‑hand‑over ceremony concluded the recorded segment.

Key Takeaways

• Data’s role has shifted from static service‑delivery inputs to real‑time, predictive assets that guide adaptive interventions, especially after the pandemic.
• Siloed data and lack of community participation are the biggest practical obstacles; participatory data pipelines are essential for AI impact.
• Governance without law – Low‑maturity countries can adopt principled, soft‑governance frameworks to get started while legislation catches up.
• Use‑case‑first approach ensures that AI tools address concrete policy challenges before governance structures are formalised.
• Operational interoperability (shared processes, trust frameworks) is as critical as technical standards for cross‑border data exchange.
• Capacity‑building must be holistic, integrating both governance training and community‑level skill development to avoid superficial, one‑off workshops.
• The Nefele demo showcased a prototype that turns project documents into searchable, AI‑augmented knowledge (vector DB + conversational UI) and integrates geospatial visualisation.
• Secure vs. open layers: Demonstrated a two‑tiered architecture where open data powers quick demos while sensitive data is handled in a slower, controlled environment.
• Future vision includes AI‑agent ecosystems (Maltbook) that can autonomously negotiate data access, requiring trust policies and regulatory sandboxes.
• Job creation hinges on meaningful, long‑term capacity‑building and co‑creation with practitioners, not on short‑term technology pushes.
• Inter‑governmental collaboration (e.g., German‑Indian pilots) benefits from shared rule‑books, sectoral data collaboratives, and open‑source foundations that lower entry barriers.

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Prepared by the AI Summarizer & Analyst, based on the verbatim transcript of the “Unlocking Impact” workshop at the AI Impact Summit, Delhi (2024).

See Also:

• thriving-with-ai-human-potential-skills-and-opportunity
• ai-for-economic-development-and-social-good