Welfare for All: Ensuring Equitable AI Growth Across the World’s Largest and Oldest Democracies

Abstract

The panel explored how the world’s leading AI practitioners can cooperate with governments to democratise the benefits of artificial intelligence. Topics included the role of international standards, localisation of AI tools for diverse languages, public‑private collaborations for workforce up‑skilling, security and trust challenges, and concrete initiatives (e.g., Google’s GenBench, Microsoft’s Elevate, Rapid7’s AI‑security framework) aimed at narrowing the digital divide between the Global North and South. The discussion concluded with reflections on the rapidly changing AI landscape, the need for adaptable regulation, and concrete steps to ensure AI‑driven growth delivers broad‑based welfare.

Detailed Summary

• Brad Staples opened with a warning that, if current trends continue, up to 70 % of AI’s economic value could be concentrated in a handful of Western countries and China.
• He stressed that this outcome is not inevitable; achieving “democratic access to AI” will require intentional design, international collaboration, workforce development, private‑sector partnerships, and robust trust‑safety mechanisms.

2. International Standards & Evaluation Frameworks

2.1 Lee Tiedrich on the Second International AI Safety Report

• The report, produced by ~100 experts, shows progress in evaluation metrics but also a significant gap between research and real‑world deployment.
• Existing standards (e.g., ISO 42001) are a good start, but speed of adoption and cultural localisation are critical.
• Tension: standards must be border‑agnostic for technology flow yet customisable for language, culture, and regulatory contexts.

2.2 NIST & Pre‑Standard Initiatives

• Lee recounted a year at NIST, working on a “zero draft” intended for ISO adoption.
• Stakeholder input is being gathered globally.
• Other nascent efforts include the Hiroshima AI Process and various AI Safety Institutes (ASIs) that enable cross‑regional collaboration.

2.3 Key Insight

International standards are essential scaffolding, but they must evolve rapidly and be adaptable to local linguistic and cultural realities.

3. Localisation, Continuous Auditing, & Public‑Private Co‑Creation

3.1 Sachin Kakkar (Google)

• Localized benchmark: GenBench, a test‑bench for fine‑tuning LLMs across 29 Indian languages, 12 scripts, and four language families.
• Copy‑pasting regulations is ineffective; each market requires tailored standards.
• One‑time audits are insufficient; AI systems need continuous scanning to avoid “temporal drift”.

3.2 Collaboration Model

• Three pillars for bridging the AI divide:
  1. Open‑source frameworks & interoperable standards (e.g., Secure AI Framework (SAIF), CoSci – Coalition of Secure AI Framework).
  2. Capacity building – sharing threat‑intelligence tools like SynthID (watermarking for AI‑generated media).
  3. Workforce up‑skilling & research – grants to Indian institutes (e.g., IITs) for post‑quantum cryptography (PQC) and other frontier research.

3.3 Trade‑off Between Global Standards & Innovation

• Global standards must avoid becoming barriers for Indian startups that face bandwidth constraints and linguistic diversity.
• The solution is an adaptive, “creative tension” approach that starts with global baseline rules but evolves locally.

3.4 Lee’s Reflection

• Evaluation first, regulation later: develop technical evaluation frameworks before deciding on mandatory regulation, recognising that regulators may lag behind rapid AI advances.

4. Public‑Private Partnerships for Skills Development

4.1 Amanda Craig‑Deckard (Microsoft)

• Microsoft Elevate – a holistic programme targeting hard‑infrastructure, skilling, multilingual AI, local deployment, and diffusion measurement.
• Commitments:
  • 10 million Indians up‑skilled by 2030 → 5.6 million up‑skilled already, later upscaled to 20 million.
  • Elevate for Educators – partnerships with schools, vocational institutes, higher‑education entities; co‑design curricula with Indian ministries.
• Approach: blend cloud compute access, AI tool exposure, and continuous learning pathways.

4.2 Amit Chadha (LTTS)

• Three‑fold strategy:
  1. Curriculum co‑creation with final‑year college programs; employees teach via CSR hours and NASCOM collaborations.
  2. Upskilling current workforce while billable – embed learning into project work rather than after‑hours.
  3. Incentivising personal‑time innovation – patents, papers, symposium talks; 52 % of staff now spend personal time on tech development (up from 19 %).
• Outcomes: patents per year rose from 50 to 200.

4.3 Julian Waits (Rapid7) – “Carrot vs. Stick”

• Carrot‑heavy approach: weekly internal tips, hackathons for non‑technical groups (e.g., legal), budget allocations for training, productivity targets (from 73 % to 83 % utilisation).
• Contrast with “stick”: mandatory productivity metrics (e.g., “double output by 2025”) are less effective; empowerment via recognition of patents, papers, speaking engagements works better.

4.4 Panel Discussion on Incentives

• Consensus that positive incentives (carrots)—recognition, budget support, integration into daily workflow—are more sustainable than punitive mandates.

5. Security, Trust, and Multilingual AI

5.1 Julian Waits (Rapid7) on Emerging Threats

• Agentic AI multiplies attacker speed ~1,000× compared with traditional ML attacks.
• Security posture must be two‑layered:
  1. Proactive controls – hardening infrastructure, supply‑chain vetting, vulnerability management across cloud, mobile, and home networks.
  2. Reactive/compensating controls – rapid incident response, employee awareness, continuous monitoring.
• Emphasis on human education: workers must transition from “programmers” to “prompt engineers”.

5.2 Amanda Craig‑Deckard (Microsoft) on Multilingual Robustness

• Multilingual AI is both a capability and a security vector: low‑resource languages can be exploited for prompt‑injection jailbreaks.
• ML Commons jailbreak benchmark: expanded from English‑only to include Indic and other Asian languages.
• Public‑private partnerships are crucial to develop robust multilingual models and measure diffusion of safeguards.

5.3 “Self‑Defending AI” (Unnamed panelist – likely Saurabh Kumar Sahu)

• Vision of AI‑versus‑AI: develop self‑defending, immune‑system‑like agents that protect critical infrastructure (hospitals, energy grids).
• Goal: shift the defender’s advantage by automating 80 % of routine defensive tasks, leaving humans to handle the remaining 20 % high‑level decisions.

6. Reflections & Forward‑Looking Perspectives

Speaker	Key Reflection
Lee Tiedrich	Global cooperation across government, industry, academia, and civil society is essential; AI can help achieve UN Sustainable Development Goals if data sharing standards (e.g., Creative Commons‑type licences for data) are established.
Julian Waits	Skills become obsolete within five years; only continual learning can keep the workforce relevant.
Amit Chadha	India’s AI impact should be grass‑root focused (farmers, schools, NGOs); AI must be an architect, not merely a consumer.
Unnamed panelist (Alan?)	India has transitioned from back‑office to front‑office for AI, becoming a global AI hub rather than a cost base.
Moderator (Brad)	The summit now integrates opportunity‑focused dialogue with safety‑first governance, exemplified by recent Indian legislation mandating AI‑generated content labelling.
Audience members (Lee & others)	Emphasised the need for AI literacy and critical‑thinking skills across the whole population, not just technical experts.

7. Audience Q&A Highlights

1. Speed of AI vs. Upskilling (question by a lady from the audience) – Lee argued that AI literacy and problem‑solving fundamentals are the core defenses against rapid displacement.
2. Digital Divide (question by Rita Soni) – Amanda reiterated Microsoft’s five‑area investment plan (infrastructure, multilingual AI, local use‑cases, diffusion measurement) and cited a blog tracking progress; she highlighted last‑mile connectivity, energy access, and community‑driven pilots (e.g., agriculture AI pilots).
3. Carrots vs. Sticks – Julian clarified that budget‑driven incentives and recognition programmes (patents, publications) outperform hard productivity mandates.

Key Takeaways

• AI value concentration is not predetermined; deliberate policy, standards, and collaboration can redistribute benefits.
• International standards must be fast‑moving and culturally adaptable; ISO 42001 is a starting point, but local language and norm customisation are essential.
• Google’s GenBench demonstrates the importance of language‑specific benchmarking for LLMs in emerging markets.
• Public‑private co‑creation (open‑source frameworks, capacity‑building tools like SynthID, and joint research grants) is the most effective route to bridge the AI divide.
• Microsoft’s Elevate programme aims to up‑skill 20 million Indians by 2030, combining cloud access, curriculum design, and partnership with ministries.
• LTTS’s three‑pronged workforce strategy (curriculum co‑creation, on‑the‑job up‑skilling, personal‑time innovation incentives) has quadrupled patent output.
• Rapid7’s “carrot” approach—weekly tips, hackathons, budget‑protected training—drives 10 % productivity gains without punitive mandates.
• Agentic AI accelerates attack speed dramatically; security must combine proactive hardening with continuous employee education on prompt‑engineering.
• Multilingual robustness is a security imperative; expanding jailbreak benchmarks to Indic languages mitigates language‑based attack vectors.
• Self‑defending AI agents could overturn the classic “defender’s dilemma” by automating the bulk of routine defence work.
• Continuous evaluation frameworks should precede regulation; otherwise, rules risk being obsolete or overly restrictive.
• AI literacy and problem‑solving fundamentals are the long‑term safeguard against rapid displacement across all economies.
• India’s role is shifting from a back‑office cost centre to a front‑office AI innovator, influencing global AI deployment and standards.

---

End of summary.

See Also:

• practical-aspects-of-using-ai-in-daily-life
• flipping-the-script-how-the-global-majority-can-recode-the-ai-economy
• democratizing-ai-requires-a-free-and-open-internet
• ai-innovation-and-collaboration-shaping-resilient-economies
• ai-for-democracy-reimagining-governance-in-the-age-of-intelligence
• implementing-ai-standards-for-global-prosperity-in-an-era-of-agentic-ai
• governing-safe-and-responsible-ai-within-digital-public-infrastructure
• ai-and-the-new-frontier-for-economic-progress-linking-innovation-to-inclusive-growth
• democratizing-ai-compute-and-digital-data-infrastructures
• ai-beyond-moonshots-a-playbook-for-many