Exploring a Regulatory Framework for Open Data

Abstract

The panel examined how India can move from voluntary, fragmented open‑data initiatives to a statutory, regulatory framework that mandates standardized, AI‑ready data sharing across government bodies. Speakers debated the trade‑offs between openness and safeguards, the need for metadata, APIs and interoperable standards, and the geopolitical and economic stakes of data sovereignty. The discussion highlighted concrete policy gaps, the role of private‑sector trust frameworks, and the importance of aligning legal architecture with capacity‑building to turn open data into a public‑good infrastructure for AI‑driven growth.

Detailed Summary

The moderator began with a three‑minute dramatisation: the UK Prime Minister “Jim Hacker” and his cabinet discuss the panel’s importance, contrasting glamorous AI‑frontier topics with the “plumbing” of open data. The vignette underscored three key tensions that would recur through the session:

1. Voluntary vs. statutory sharing – without legal teeth, data provision is uneven.
2. Innovation versus consistency – a lack of standards discourages investors and developers.
3. Privacy, security, and geopolitical risk – open data must be balanced against sovereign concerns.

Transition: The moderator then invited Dr Shashi Tharoor for the keynote.

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2. Keynote – Dr Shashi Tharoor

2.1 Framing Open Data in the AI Age

• Data as a “triad” – commerce, communication, cognition now constitute the core of national wealth.
• Myth‑busting – citing Chip War, Tharoor argued that processing power (not raw data volume) is the real bottleneck; sheer openness without capacity can worsen inequality.

2.2 Power, Sovereignty, and Inclusion

• Open‑data regulation is a question of power – who extracts value, who is left behind.
• Emphasised the need for structured openness that safeguards privacy, copyright, and national security while encouraging innovation.

2.3 Call to Action

• Invited panelists (including “Shival Shroth”, “Asha Jadeja Motwani”, etc.) to co‑design a framework that balances ambition with anxiety about AI.

Key Insight: Open data is not a technical footnote; it is a foundational public infrastructure that must be governed consciously.

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3. Panelist Contributions

3.1 Rama Vedashree – History & Evolution of India’s Open‑Data Landscape

• The National Data Sharing & Accessibility Policy (NDSAP) originated in 2012, driven by senior civil servants (Kapil Sibal, Sachin Pilot) and industry input.
• Initial focus was government‑research data, pre‑startup/AI era; platforms delivered PDFs/CSVs rather than AI‑ready, API‑driven streams.
• Current gap: metadata, standards, and real‑time APIs are missing; “dark data” remains siloed in agencies such as NPCI or CERT.
• Recommendation: Shift from “download‑and‑analyse” to interactive, AI‑compatible services; open‑data must be discoverable, interoperable, and consumable by both applications and large language models.

3.2 Irina Ghosh (Anthropic India) – Trust‑First Innovation

• Stressed trust as a measurable outcome; questioned whether Indian data pipelines are trustworthy for AI.
• Launched Economic Impact Survey showing India is Anthropic’s biggest Claude user base.
• Introduced Model Context Protocol (MCP, 2024) – a universal connector for data domains (agriculture, health, finance) akin to a universal phone charger.
• Emphasised localisation: datasets must be language‑ and domain‑specific (e.g., Hindi‑agri, regional‑health).
• Commitment to transparent data sharing aligned with the Prime Minister’s digital‑economy manifesto.

3.3 Cyril Shroff – The Need for a Tailored Regulatory Overlay

• Asserted a regulatory framework is essential to overcome departmental silos and uneven incentives.
• Warned against over‑regulation (e.g., a Europe‑style GDPR) that could stifle innovation; the goal is a foundational stone for AI‑ready data, not a barrier.
• Reiterated data sovereignty: most raw data originates in the Global South, value is captured in the Global North; India must assert rights.
• Mentioned his Cyril Shroff Center for AI Law & Regulation as a platform for proactive legal design.

3.4 Dr Sasmit Patra – Evidence‑Based Policymaking & Political Realities

• Confirmed data is indispensable for policy – e.g., predicting crop‑loss hotspots for the Pradhan Mantri Fasal Bima scheme.
• Highlighted federal data silos (state vs. centre) that impede unified AI analysis.
• Raised the political question: Will citizens consent to share anonymised UPI or health data for LLM training?
• Suggested that public trust is the linchpin; policy must be transparent and voluntary where possible.

3.5 Follow‑up by Rama Vedashree (implementation gaps)

• Urged a supply‑demand gap assessment: identify who will consume which datasets (researchers, startups, regulators).
• Pointed to sector‑specific openness: payment‑systems directive (UK), EU’s FIDA for finance, upcoming Aarogya Bharat health data opening.
• Emphasised federated, not monolithic, repositories; sectoral data‑opening policies must coexist with a national strategy.

3.6 Arun Prabhu – Legal Architecture for Sustainable Open Data

• Identified four missing legal ingredients in India:
  1. Standardised anonymisation methodology
  2. Public‑data interchange standards
  3. Clear purpose‑definition for public‑good processing
  4. Coherent alignment with sectoral regulations (avoiding legal fragmentation)
• Warned that without these, LLM projects risk “judicial storms” and “legislative climate change”.
• Cited the Puttaswamy judgment as a constitutional anchor for any future data law.

3.7 Cyril Shroff – Economic Impact of Reliable Public Data

• Drew an analogy with India’s capital‑markets evolution: regulatory clarity, enforcement, and uniform accounting standards drove IPO growth.
• Argues the same principles apply to data: consistent rules → investor confidence → data‑centre investments → shift from services‑to‑product‑based tech sector.
• Stressed that trust is impossible without transparent information and enforceable legal frameworks.

3.8 Dr Sasmit Patra – Geopolitical Dimensions

• Distinguished three data categories: public‑good, national‑security, commercially monetisable.
• Proposed tag‑based governance so each dataset receives the appropriate access tier.
• Mentioned ongoing debates on a “soft‑touch” AI regulation (not a hard‑EU‑AI‑Act) that balances innovation with security.

3.9 Closing One‑Liners

Speaker	Key Closing Thought
Rama Vedashree	India needs a federated, sector‑aware data strategy to become a global AI thought‑leadership hub.
Cyril Shroff	India must craft home‑grown regulations (not copy‑pasting the West) that give certainty to investors.
Irina Ghosh	Trust is the last mile; it is woven by contextual, open, and co‑contributed data nets.
Arun Prabhu	Absence of a legal framework turns inconvenience into a structural impediment for a data economy.
Asha Jadeja Motwani	If India chooses the American tech stack, a joint US‑India regulatory framework is essential to safeguard reciprocal benefits.

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4. Audience Q&A

Question	Respondent(s)	Summary of Answer
Who will watch the watchers? (concern about AI‑powered regulators)	Cyril Shroff (with support from the audience)	The courts are the ultimate constitutional watchdog; ethics boards and industry self‑regulation complement the legal system.
Will AI‑driven data improve farmer outcomes? (addressed to Dr Patra)	Dr Sasmit Patra	With granular, openly shared data, predictive AI can anticipate crop loss, suggest diversification, and design mitigation plans. Success hinges on data availability and farmer adoption.
How to enable precise, sensitive data (e.g., men’s mental‑health) for research without breaching privacy?	Rama Vedashree (follow‑up from panel)	Suggests opt‑in anonymised sharing, similar to Germany’s health‑data provisions; regulation must be progressive, consent‑driven, and paired with awareness campaigns.

Overall Observation: The Q&A reinforced the recurring themes of trust, consent, and institutional oversight.

Key Takeaways

• Statutory mandates are essential to overcome fragmented voluntary data sharing and to provide legal certainty for AI developers and investors.
• AI‑ready open data must be API‑driven, richly meta‑dataed, and interoperable; static CSVs/PDFs are insufficient.
• Sector‑specific openness (finance, health, agriculture) with clear access tiers (free, paid, restricted) will align incentives and protect privacy.
• Data sovereignty: While openness fuels innovation, India must ensure that value generated from its data stays within the country or is shared under fair terms.
• Legal architecture gaps – anonymisation standards, data‑interchange protocols, purpose‑definition, and harmonisation with sectoral rules – must be filled to avoid regulatory uncertainty.
• Trust is the linchpin: Transparent governance, enforceable rules, and independent ethics bodies are required to “watch the watchers”.
• Capacity building is non‑negotiable; open data should reinforce domestic research, startups, and digital infrastructure rather than simply export raw datasets.
• Geopolitical considerations: Data policy must balance global cooperation (e.g., US‑India tech stack) with national security and commercial interests.
• Economic argument: Just as clear capital‑market regulation propelled India’s IPO boom, a coherent open‑data regime will attract data‑centre investment, enable product‑centric tech firms, and boost long‑term growth.
• Inclusive vision: Open data should be treated as public infrastructure, supporting democratic accountability, crisis response (e.g., COVID‑19), and equitable AI benefits across all societal strata.

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See Also:

• publicly-accessible-data-and-ai-training-safeguards-for-responsible-reuse
• data-sharing-infrastructures-for-ai-building-for-trust-purpose-and-public-values
• local-voices-first-why-inclusive-ai-for-data-systems-must-start-on-the-ground
• navigating-the-ai-regulatory-landscape-a-cross-compliance-framework-for-safety-and-governance
• pathways-for-equitable-ai-compute-access
• scaling-trusted-ai-for-8-billion
• democratizing-ai-resources-in-india
• building-ai-for-bharat-from-innovation-to-outcomes
• sovereign-ai-infrastructure-for-bharat-and-global-south
• artificial-general-intelligence-a-new-paradigm-of-safety-security-privacy-ethics-and-governance