AI as an Opportunity for More Impactful Open Data

Abstract

The panel examined how artificial intelligence can amplify the impact of open public data. It traced the evolution of “open‑data waves,” warned of a looming “data winter,” and explored how AI can both democratise data access and improve data quality. Panelists highlighted the unique role of national statistical offices (NSOs) as trusted stewards of high‑quality data, outlined the technical prerequisites for AI‑ready datasets (metadata, standards, provenance), and debated the policy and governance challenges—particularly in low‑resource contexts—needed to ensure that the benefits of AI accrue to citizens rather than only to commercial AI firms. The session closed with a rapid Q&A covering bias, metadata design, open‑innovation policy, and the distribution of value from public data.

Detailed Summary

Mercedes Fogarassy (Moderator) – welcomed the audience, introduced the theme of “AI for economic development and social good,” and stressed that AI’s transformative potential hinges on high‑quality, trusted, well‑governed public data.

Stefan Verhoest (Remote video) – outlined the four “waves” of open data:

1. Freedom of information (first wave)
2. Government portals publishing data (second wave)
3. Private‑sector contributions for public‑interest services (third wave)

He warned that despite an “AI summer,” a “data winter” is emerging—characterised by reduced data accessibility. He argued that generative AI can both democratise data and benefit from open data, ushering in a fourth wave where AI and open data intersect. He cited use‑cases such as:

• AI‑enhanced disaster‑response tools
• Conversational data interfaces (chatbots) that lower the skill barrier for data querying
• Synthetic data generation to fill gaps where data are scarce, sensitive, or unrepresentative
• AI‑driven data‑quality checks (e.g., automated data‑contract compliance)

He concluded that “the fourth wave” must balance AI‑readiness (updating FAIR principles) with trust and institutional innovation (e.g., data commons).

Transition: Mercedes thanked Stefan and moved the discussion toward the institutional side of the debate.

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2. Role of National Statistical Offices (NSOs) – François Fonteneau

• Three core contributions of NSOs

  1. Quality fuel for AI – NSOs have long produced validated, comparable, time‑series data essential for training reliable models.
  2. Structure & standards – They provide machine‑readable classifications, metadata, and consistent schemas (e.g., SDMX) that make data ingestible by AI systems.
  3. Legal‑ethical framework – NSOs operate under strict confidentiality, neutrality, and public‑interest mandates, which are increasingly vital in an AI‑driven ecosystem.

• Opportunities for AI in NSOs

  • Data production – AI (e.g., satellite‑imagery analysis) can accelerate the creation of granular, timely statistics (example: partnership with Google on population and housing data in Mongolia).
  • Data access – AI‑powered chatbots can deliver official statistics to policymakers, journalists, and the public in natural language, democratizing insights.

• Key message: AI will reward NSOs that are modern, open, structured, and trusted; it will threaten those that remain siloed or under‑invested.

Transition: Mercedes invited the NSO representative from India to expand on the evolving mandate.

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3. Evolving Mandate of NSOs – Rohit Bhardwaj (NSO India)

• Dual role: NSOs cannot be exclusively data producers or data stewards; they must combine both.

• Four pillars of the evolving role

  1. Data Governance – establishing national metadata standards, quality frameworks, and classification systems.
  2. Validation of administrative data – ensuring that newly sourced datasets meet the statistical quality required for AI‑driven analysis.
  3. Ecosystem orchestration – coordinating with state governments and other data providers (e.g., the upcoming 24‑May outreach to state governments).
  4. Ethics & Trust – maintaining confidentiality, neutrality, and public‑interest orientation while fostering AI readiness.

• Infrastructure developments

  • MCP (Metadata Catalog & Publication) server – a cloud‑compatible platform that lets users plug into NSO‑hosted datasets via APIs.
  • Digital dissemination bouquet – an integrated suite of tools (including micro‑data portals) to ensure data are discoverable and usable.

• Conclusion: NSOs must be mixed‑role actors, producing unique statistical outputs while also curating, validating, and democratizing data for AI consumption.

Transition: Mercedes asked the technical perspective on making datasets AI‑ready.

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4. Technical Prerequisites for AI‑Ready Open Data – Randeep Toor (Google – Data Commons)

1. Rich Metadata – Detailed descriptive information (provenance, licensing, variable definitions) that enables AI agents to understand context.
2. Interoperable Standards – Adoption of schemas such as schema.org, SDMX, and MDS, plus building adapters that allow different standards to “talk” to each other.
3. Provenance & Versioning – Recording the origin, transformation history, and usage constraints of each dataset, analogous to a nutrition label on food.

These three pillars together ensure that data can be scored, trusted, and reused at scale by AI systems.

Transition: Mercedes thanked Randeep and turned to the philanthropy perspective.

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5. Philanthropic View & Global‑South Realities – Christopher Maloney (Hewlett Foundation)

• From data‑revolution to AI‑revolution – Over four years, the focus has shifted from data governance to AI governance, yet the underlying challenges remain: connectivity, electricity, capacity, multi‑stakeholder governance, sovereignty, and accountability.

• Fundamental data gaps – Need for local data, local languages, and trust‑safety mechanisms to avoid reinforcing the North‑South divide.

• Trust as a cornerstone – Official statistics must retain their status as the “trusted reference point” for policy decisions.

• Risks:

  • Synthetic‑data flood could erode public trust if not managed.
  • Platform substitution (replacing expert knowledge of WDI/Comtrade with black‑box AI tools) may create a “knowledge death‑spiral.”
  • Budgetary pressure – AI hype could divert funds away from essential surveys and censuses.

• Hopeful signals: Emerging citizen‑generated data initiatives that bring rigor and local voice to the data ecosystem.

Transition: Mercedes asked Rohit to comment on ensuring trust when AI systems generate insights.

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6. Trust, AI‑Readiness, and Public Engagement – Rohit Bhardwaj

• AI‑readiness = Data + Metadata – Trust is built when AI queries are directed to the official NSO database rather than arbitrary web sources.

• Implementation steps at NSO India:

  • Provision of complete metadata via the MCP server.
  • Ensuring interoperability (so AI agents can ingest data).
  • Promoting public data literacy so users know when to use AI vs. the official portal.

• Conclusion: Trust must be cultivated beyond legal safeguards; it rests on transparent metadata, interoperable infrastructure, and citizen literacy.

Transition: Randeep supplied a concrete example of AI improving data usability.

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7. Concrete Example – Randeep Toor

• Data Commons aggregates datasets from NSOs, aggregators, and government agencies into a single interoperable format enriched with metadata.

• Natural‑language interface allows users to ask questions such as “What is the correlation between diabetes prevalence and food scarcity?” – the system then queries the unified data graph and returns a data‑driven answer.

• Google also applies the same metadata‑enrichment → standardisation → provenance pipeline internally for enterprise data, demonstrating “walking the talk.”

Transition: François reflected on the global statistical community’s adaptation.

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8. Global Statistical Community – François Fonteneau

• Recognition of AI’s permanence – The community is actively updating standards, training staff, and piloting AI‑ready initiatives (e.g., India’s MCP server).

• Challenges:

  • Funding cuts for leading NSOs (budget reductions up to one‑third).
  • Statistical literacy decline (OECD data showing falling numeracy).

• Positive actions:

  • Citizen‑centric outreach to raise awareness of high‑quality data.
  • Aggressive piloting & wise deployment of AI tools while avoiding hype.

Transition: Christopher offered a synthesis on whether we are entering a renaissance or a risk‑laden era.

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9. Renaissance vs. Undermining – Christopher Maloney

• Potential Renaissance:

  1. High‑quality data + AI → reduced hallucinations, bias mitigation.
  2. Vibrant ecosystem of researchers, startups, civic innovators can layer AI on open data.
  3. Democratization – non‑experts can query and interpret statistics via natural language.

• Potential Undermining:

  1. Synthetic‑data overflow may erode trust if unchecked.
  2. Platform substitution – over‑reliance on AI tools could diminish deep statistical knowledge.
  3. Funding diversion – governments might cut back on surveys, assuming AI can “fill the gaps.”

• Illustrative tension: In Tanzania, publishing unauthorised statistics could lead to jail; in Uganda, data mis‑classification triggered political fallout. AI could amplify both repression and citizen‑generated data.

Transition: Mercedes introduced a provocative audience question about the distribution of benefits.

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10. Audience Q&A (selected questions & panel responses)

Question	Panelist(s) & Summary of Answer
Bias & reliability standards (Prima Ganguly – Thapa University)	Rohit emphasized NSO India’s metadata‑driven validation pipeline and quality frameworks that check for bias before data are released for AI use.
Priority metadata components (Sharad – DataKind)	Randeep listed essential tags: provenance, licensing, temporal/spatial granularity, statistical methodology, variable definitions, and quality indicators. He stressed that the set should be as exhaustive as AI can process.
Granularity of metadata for sensor/IoT data (Ikanj – researcher)	Rohit responded that “enough” granularity is use‑case dependent; for high‑frequency sensor data, include sampling rate, instrument calibration, location, and error bounds—metadata that allows reproducibility and cross‑verification.
Open‑innovation sandboxes & distributed data governance (Dolly Hussain)	Randeep argued that open‑source APIs (e.g., Data Commons) and standard‑based licensing enable sandbox environments. Governance requires clear data‑use agreements and multi‑party stewardship models to avoid a single point of control.
Provocative: Who benefits more—AI firms or citizens?	Randeep warned of asymmetric power dynamics (e.g., Kenya‑US health data deal, WorldCoin iris‑scanning) and called for context‑specific data‑contribution agreements and fair‑value mechanisms.
Rohit added that OpenData is not “free”; the cost of collection must be acknowledged, and policies should ensure benefit‑sharing (e.g., cost‑recovery models).
Christopher highlighted the need for robust local ecosystems so that private firms cannot dominate the value chain.
Additional quick question on metadata tags (Rohit)	Emphasised that metadata should be as rich as the AI pipeline can digest—no fixed number of tags; the goal is machine‑readability and completeness.

The panel also noted time constraints and indicated that deeper technical details (e.g., exact schema mappings) would be shared in follow‑up resources.

Closing: Mercedes thanked the panel and audience, reiterated the importance of multi‑stakeholder collaboration, and invited participants to explore the AI Course and OpenData portals provided by the Government of India.

Key Takeaways

• AI amplifies the value of high‑quality public data but cannot compensate for poor data; NSOs remain the essential source of trusted statistics.
• Four pillars for NSOs in the AI era: data governance, validation of administrative data, ecosystem orchestration, and ethical trust‑building.
• Three technical prerequisites for AI‑ready open data: (1) rich, machine‑readable metadata, (2) interoperable standards (schema.org, SDMX, MDS) with adapters, and (3) full provenance & versioning.
• Public‑data accessibility must be paired with data‑literacy initiatives so citizens know when to trust AI‑generated answers versus official portals.
• Synthetic data and platform substitution pose risks: they can erode trust and diminish statistical expertise if not carefully managed.
• Funding pressures are real; AI hype should not replace essential surveys, censuses, and statistical capacity building.
• Citizen‑generated data can complement official statistics, but must be integrated with robust validation to avoid quality dilution.
• Benefit‑sharing is critical: without clear data‑contribution agreements, AI giants may capture disproportionate value from publicly funded data.
• Global statistical community is actively updating standards and piloting AI‑ready workflows, despite budget cuts and declining statistical literacy in some regions.
• OpenData is not “free” in the sense of zero cost; acknowledging collection costs and instituting fair‑use policies ensures sustainable open‑data ecosystems.

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See Also:

• flipping-the-script-how-the-global-majority-can-recode-the-ai-economy
• welfare-for-all-ensuring-equitable-ai-growth-across-the-worlds-largest-and-oldest-democracies
• responsible-ai-for-health-governance-implementation-and-investment-considerations