Making AI All Inclusive: Bridging AI Communities to India’s AI Future

Abstract

The session opened with a short video illustrating the vision of JAN AI and introduced a new “Youth Growth‑Resilience‑Aspirations‑Future‑Readiness” (U‑graph) report that captures AI awareness and usage among 3 000 rural youth across 20 Indian states. The panel then explored how AI can be made inclusive for all Indian communities. Topics covered ranged from gender‑responsive design and governance, to micro‑AI agents for every citizen, to real‑world deployments such as Digital Green’s farmer‑chatbot. Panelists highlighted the necessity of culturally‑aware, low‑resource AI tools, the importance of data that represents women and marginalized groups, and the role of collaborative ecosystems (GEN AI collaborative, open‑source projects) to accelerate equitable AI adoption.

Detailed Summary

• A creative video (produced by a Jaipur‑based youth team) was shown, linking the historic “spinning wheel” of self‑reliance to today’s “digital chakra” – JAN AI – that aims to let every Indian village shine.
• Madan Padaki (moderator) announced that JAN AI has 100+ collaboration partners, 30 of whom were present at the pavilion.
• The team highlighted a three‑month field study: ≈3 000 rural youth across 20 states were surveyed on AI awareness, usage, fears, and aspirations.
• Key findings from the U‑graph report (released live):
  • 55 % of respondents were aware of AI.
  • 55 % reported daily AI usage; 17 % used it only occasionally.
  • Significant concerns were expressed around data privacy, misinformation, and gender‑based harms.
• The report was co‑produced with Gates Foundation, Nudge, GXD Hub, CL Educate, and MySati. QR codes for download were displayed, and the audience was encouraged to spread the findings to the “Bharat Generation”.

2. Panel Introduction & Moderator’s Opening Question

• Madan Padaki framed the core question: “How do we bridge communities to use AI in a meaningful, inclusive way?”
• He introduced each panelist:
  1. Prof. Ramesh Raskar – MIT Media Lab, leading “Project Nanda”.
  2. Kanta Singh – UN Women Country Representative, champion of women‑focused STEM/AI programs.
  3. Sri Rajan – Bain & Co, former chairman of Bain India, board member of Akshay Patra.
  4. Nidhi Bhasin (Digital Green) – AI for agriculture and farmer advisory services.
  5. (Absent) Mr. Jayant Rastogi – MagicBus (not present).

3. Inclusive Design & Governance (Kanta Singh)

• Emphasised that design solutions must be co‑created with the people they serve, especially women, disabled persons, and other marginalized groups.
• Stressed that ≥ 50 % of the surveyed youth should be women to capture gender‑specific needs.
• Cited a disturbing report showing young tribal women being exposed to exploitative porn‑derived content as a stark example of gender‑biased AI harms.
• Called for gender‑responsive device design, transparent AI governance, and participatory rule‑making so that women’s voices shape AI policy.

4. Democratizing AI Agents (Prof. Ramesh Raskar)

• Re‑positioned AI not merely as a “social‑impact” project but as a “blue‑ocean” economic opportunity that can attract top talent away from high‑margin tech firms to traditionally low‑margin sectors (water, ports, agriculture).
• Described Project Nanda: a platform to create personal micro‑AI agents (“your own AI”) for every citizen, reducing dependence on centralized AI “masters”.
• Announced two open‑source portals: projectnanda.org and digiduth.org (the latter a “layer‑of‑layer” AI‑agent infrastructure).
• Projected that 1.4 billion potential users could each have multiple AI agents, dramatically expanding productivity across the Indian economy.

5. AI for Farmers & Last‑Mile Connectivity (Nidhi Bhasin / Digital Green)

• Highlighted farmer.chat, a chatbot that has reached ≈ 1 million users (≈ 600 k in India) across 8 states.
• Stressed that AI adoption is less about model access and more about trusted decision‑support that fits local languages, socio‑economic contexts, and geography.
• Noted 45 % of farmer.chat users are women because men often migrate for work, leaving women to manage households and farms; women value discreet, judgment‑free advice.
• Identified three layers of divide: digital, language/social‑economic, and gender; solutions must address all three.

6. Education, Youth & AI Tools (Sri Rajan)

• Shared findings from an upcoming 12 500‑household, 2 500‑teacher survey (Central Square Foundation) showing:
  • Access is no longer the bottleneck; low‑income households already use smartphones heavily.
  • YouTube is the top ed‑tech tool; WhatsApp is the top AI tool for children.
• Highlighted the rise of personalized adaptive learning (PAL) and the potential to embed AI‑driven “scheme‑matching” for seniors, women, and villagers.
• Pointed out the shockingly low female representation (≈ 18 %) in India’s corporate workforce and urged AI‑driven platforms to open market access for women‑led enterprises.

7. Gender‑Intentional Design & Livelihood Augmentation (Chetana – Nudge)

• Described three focus areas:
  1. Career‑pathway formalisation for women in AI‑related jobs.
  2. Gender‑intentional UI/UX design (safe, comfortable, culturally appropriate).
  3. AI‑augmented livelihoods – e.g., farmer.chat enhancing agricultural decision‑making.
• Invited partners to collaborate on these initiatives.

8. Global Perspective & Education on AI Agents (Chris Peas)

• Noted that U.S. discussions often centre on technology, while Indian participants emphasise people‑centric concerns.
• Asserted that if citizens will interact with 50+ AI agents, a mass‑scale education programme on “leadership and management of AI resources” is essential to realise benefits.

9. Audience Q&A (selected highlights)

Question / Theme	Key Responses
Future of AI‑generated content (health misinformation)	Panelist cautioned that many LLM errors will be fixed by tech advances; suggested focusing on longer‑term societal impacts rather than short‑term bugs.
Skilling and data capture for women	Emphasised that skill‑building must be paired with robust data collection to ensure women are represented in training sets and policy dashboards.
Sovereign vs. culturally‑aware AI	Consensus that AI must be locally contextualised (language, culture) even if not fully sovereign; low‑data, mobile‑friendly models are priority.
Guardrails for children using AI (WhatsApp/YouTube)	Agreed on need for parental controls, content moderation, and digital literacy for low‑income households.
Open‑source model for farmer.chat	Confirmed that the chatbot is fully open‑source, enabling community adaptation and localisation.
Collaboration platform	Announced GEN AI Collaborative (Hall 14) – a “one‑stop shop” for NGOs, startups, and researchers to avoid duplicated effort and co‑create solutions.

10. Closing Remarks & Announcements

• Gen AI Collaborative invites participants to visit Hall 14 to meet AI “super‑heroes” (six women, six men) from villages such as Sirohi and Raichur.
• Madan Padaki thanked the panel, announced that the U‑graph report will be shared with the board, and invited the audience to continue dialogue over the next five days.
• Asha Jadeja Motwani presented panel mementos; a group photo of the panel was displayed, symbolising the “power of transformation”.

Key Takeaways

• Inclusive AI starts with co‑creation: solutions must be built with rural youth, women, and other marginalized groups, not for them.
• Gender‑responsive design and governance are critical to prevent AI‑driven harms (e.g., exploitative content targeting tribal women).
• Micro‑AI agents for every citizen (Project Nanda) can democratise productivity gains across low‑margin sectors, turning AI into a new economic “blue‑ocean”.
• Farmer‑focused chatbots demonstrate that trusted, language‑aware decision support drives adoption; women already constitute a large user base.
• Access is no longer the main barrier; the focus now shifts to localized, low‑resource AI models and digital literacy (especially for children using WhatsApp/YouTube).
• Corporate gender imbalance (≈ 18 % women) needs AI‑enabled market‑access platforms to empower women entrepreneurs.
• Open‑source tools and collaborative ecosystems (GEN AI Collaborative) are essential to avoid duplicated effort and accelerate scalable impact.
• Education on AI stewardship (how to manage multiple agents) is as important as the technology itself for mass adoption.
• Data representation matters: without women and marginalized groups in training data, AI will perpetuate existing inequities.
• Long‑term vision over short‑term bugs: while current LLM errors will improve, policies must address deeper societal challenges (privacy, bias, livelihood impact).

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