FRUGAL AI FOR INCLUSIVE FUTURES: ENABLING GRASSROOTS INNOVATION IN HEALTH, EDUCATION, AND GOVERNANCE

59 CHAPTER-6 FRUGAL AI FOR INCLUSIVE FUTURES: ENABLING GRASSROOTS INNOVATION IN HEALTH, EDUCATION, AND GOVERNANCE Dr. M Zaheer Ahmed Assistant Professor and Head PG Department of Commerce and Research Dr Kalaignar M. Karunanidhi Government Institute for Post Graduate Studies and Research, Karaikal, U.T of Puducherry, India Abstract Artificial Intelligence (AI) can only be truly transformative when it is accessible, ethical, and rooted in the lived realities of diverse communities. This chapter explores the concept of FrugalAI a low-cost, resource-sensitive, and inclusive approach to digital innovation and its application across sectors such as healthcare, education, and governance. The study focuses on the Union Territory of Puducherry as a pilot site, leveraging its unique Indo–French legacy, multilingual population, and policy agility to design and test scalable AI solutions. By proposing the Indo–French Frugal AI Innovation Hub (IFFAIH), the chapter demonstrates how global partnerships, diaspora engagement, and grassroots participation can converge to co-create AI tools for civic services, multilingual education, telehealth, and grievance redressal. Aligned with Sustainable Development Goals (SDGs), the chapter maps existing Central and State government schemes to support implementation, ensuring cost-effectiveness and cultural relevance. Through policy recommendations, a localized roadmap, and an institutional model, this chapter positions Puducherry as a replicable testbed for ethical, inclusive, and human-centric AI innovation demonstrating that the future of AI must not only be smart, but also socially just and locally grounded. 1. Introduction In the era of rapid digital transformation, Artificial Intelligence (AI) has emerged as both a disruptor and an enabler across domains such as healthcare, education, governance, and livelihoods. While much of AI discourse is centered around hightech innovation and resource-intensive models, the pressing need in the Global South lies in developing contextual, low-cost, and scalable AI solutions that prioritize accessibility and social equity. This calls for a paradigm shift toward what is increasingly known as Frugal AI a model that combines technological efficiency with economic and cultural relevance. This chapter critically examines how Frugal AI can serve as a strategic enabler of inclusive development. It advocates for ethical, localized, and multilingual AI models that empower underserved populations, particularly in regions marked by administrative uniqueness, linguistic diversity, and historical linkages to global 60 systems. In doing so, it highlights the Union Territory of Puducherry as a living laboratory for testing AI-driven public service tools given its bilingual heritage, dynamic diaspora, and proximity to Indo–French institutional ecosystems. Grounded in the Sustainable Development Goals (SDGs), the chapter aims to propose a replicable innovation framework that links frugal technological design with grassroots application, civic engagement, and transnational cooperation. The goal is not merely to advance AI penetration, but to reimagine it as a peoplecentric, cross-cultural, and economically viable force for sustainable transformation. 2. Conceptual Framework and Literature Review 2.1 Frugal AI: A Paradigm for Inclusive Innovation Frugal AI is an emerging design philosophy that emphasizes creating resourceefficient, context-sensitive, and affordable AI solutions for underserved communities. Unlike conventional AI models developed in data-rich and capitalintensive environments, Frugal AI prioritizes simplicity, cost-effectiveness, and adaptability. Scholars such as Radjou and Prabhu (2012) have argued that frugal innovation, often born out of scarcity, can serve as a springboard for scalable transformation in developing nations. Applied to AI, this means developing tools that require minimal computational resources, rely on open datasets, and can operate across low-connectivity regions. The concept aligns closely with digital public goods, low-resource AI models, and human-centered computing, and is increasingly recognized as essential for achieving the SDGs, particularly those related to education (SDG 4), healthcare (SDG 3), gender equality (SDG 5), and industry innovation (SDG 9). It also resonates with UNESCO’s call for AI that upholds human rights, diversity, and equity. 2.2 Socio-Technological Context in India and Global South India, with its vast linguistic diversity, rural–urban divide, and dynamic governance models, presents both a challenge and an opportunity for AI localization. National programs such as the Digital India Mission, Ayushman Bharat Digital Health Mission, and PM Gati Shakti have created enabling frameworks for deploying AI in healthcare, logistics, agriculture, and citizen services. However, effective deployment is often hindered by infrastructure gaps, digital illiteracy, and linguistic barriers underscoring the need for Frugal AI tailored to local contexts. Globally, countries in the Global South are experimenting with AI-on-the-edge, community-annotated datasets, and open-source machine learning platforms to address region-specific needs. Initiatives like Smart Africa, Aapti Institute’s AI Localism reports, and GSMA’s Mobile for Development program offer valuable insight into the viability of cost-sensitive AI. 61 2.3 Cultural Context and Indo–French Opportunities Puducherry’s Franco–Indian heritage adds a unique cultural dimension to AI development. With its institutional connections to France, multilingualism (Tamil, French, and English), and access to diaspora networks, Puducherry becomes an ideal ground for transnational AI pilot projects. The potential to codevelop tools in collaboration with French universities, the Alliance Française, and diaspora technologists adds global credibility to the otherwise local experimentation setting the foundation for the Indo–French Frugal AI Innovation Hub proposed in this chapter. 2.4 Review of Frugal AI in Practice: India and the Global South Recent years have witnessed a surge in frugal technological interventions, particularly in regions where traditional infrastructure and access are limited. In India, initiatives like eSanjeevani (a telemedicine platform) and DIKSHA (a multilingual digital learning repository) exemplify how low-cost digital solutions can bridge gaps in public service delivery. These platforms operate on open standards, require minimal bandwidth, and utilize devices already available to users principles that align closely with Frugal AI design. Beyond India, several countries in the Global South have embraced similar models. For example: Smart Africa has promoted AI-driven maternal care alerts through SMS bots in Uganda and Rwanda. In Bangladesh, AI models trained on local floodplain data have helped in disaster preparedness with minimal computational cost. Kenya’s AI-powered soil diagnostic tools use basic mobile phones and crowdsourced data to deliver low-cost farming advice. These examples highlight that frugal does not mean inferior rather, it implies technological adaptation that honors constraints while expanding reach. However, these projects often remain siloed and are not integrated into broader AI-forDevelopment (AI4D) strategies or linked to global collaborations. 2.5 Identified Gaps and Rationale for the Proposed Model Despite promising case studies, several critical gaps remain in both theory and application: 1.Localization remains narrowly defined. Most Frugal AI deployments focus on regional language interfaces, yet fail to account for cultural hybridity (such as Tamil–French–English bilingualism in Puducherry) that could foster globally scalable models. 2.Diaspora engagement is largely absent. While countries like India have tapped diaspora for remittances and philanthropy, their role as AIco-innovators especially in linguistically dual or multicultural regions—remains underexplored. 62 3.Frugal AI is rarely integrated into formal governance structures. Many solutions are NGO-led or startup-initiated and lack alignment with state digital missions or local administrative systems limiting scale and sustainability. 4.No replicable institutional framework currently exists to bridge: Global ethical AI mandates (e.g., SDGs, UNESCO) Design-level innovation (e.g., open-source, low-compute models) Local governance (e.g., Digital India, Smart Cities) Cross-cultural collaboration (e.g., Indo–French cooperation) To better synthesize the discussion above, the following framework integrates three key dimensions—global digital mandates, frugal AI design principles, and Puducherry’s unique sociotechnical context. This visual model helps conceptualize how scalable, ethical, and affordable AI can be grounded in both international development priorities and localized governance ecosystems. It also provides the foundational logic for the implementation roadmap proposed later in this chapter. Source: Developed by the author based on UN SDGs, Frugal Innovation Literature, Digital India guidelines, and Indo–French cultural cooperation strategies. Figure 2.1 Conceptual Framework for Frugal AI Deployment in the Global South This figure visualizes the convergence of global digital public goods, frugal design principles, and localized socio-political assets in Puducherry. It situates Frugal AI as the focal strategy uniting international mandates (like the SDGs), cost-effective AI tools, and Indo–French collaboration opportunities. 3: Research Methodology This section outlines the methodological framework employed to conceptualize and validate the application of Frugal AI in the Union Territory of Puducherry. Given the interdisciplinary nature of the study encompassing development economics, digital governance, AI design, and regional planning a qualitative, exploratory research approach was adopted, supported by case synthesis and policy mapping. 3.1 Research Objectives • To examine how Frugal AI principles can be contextually applied in public services such as education, health, and governance. 63 • To analyze the sociotechnical readiness of Puducherry to act as a testbed for Frugal AI innovation. • To design a replicable model that aligns with global policy mandates (e.g., SDGs, UNESCO AI ethics) and local governance systems. 3.2 Research Design The research draws upon a multi-method qualitative framework, including: • Documentary Analysis: Central and State policy documents (e.g., Digital India, NDHM, U.T. Budget), Indo French MoUs, and French digital governance reports. • Case Study Synthesis: Comparative insights from AI-for-development initiatives across India, Kenya, Rwanda, Bangladesh, and France. • Theoretical Anchoring: Grounded in Rogers’ Diffusion of Innovations Theory and Sen’s Capability Approach to examine how low-cost AI interventions expand human choices in constrained environments. 3.3 Data Sources Table 3.1: Data Sources Used in the Study Data Type Sources Policy Frameworks Digital India, NDHM, PM Gati Shakti, Smart Cities Mission, Puducherry State Budget Institutional Reports Alliance Française, French Consulate, UNDP, UNESCO AI Guidelines Case Studies Published research on eSanjeevani, DIKSHA, Aapti Institute AI pilots Local Inputs (Indirect) Administrative blueprints, budget speeches, Indo French university collaborations Source: Compiled by the author based on national policy portals, institutional documents, and Indo–French academic collaborations. 3.4 Analytical Framework The following triadic analysis structure was used: 1. Input–Process–Outcome Mapping To assess how existing or proposed AI tools operate within resource-limited environments. 2. Barrier–Enabler Matrix To identify socio-cultural, technological, linguistic, and institutional variables affecting adoption. 3. Capability Enhancement Lens To examine how Frugal AI contributes to expanding agency, access, and empowerment—particularly for marginalized populations in bilingual regions like Puducherry. 64 3.5. Scope and Limitations This is a design-level study, focused on framework building and feasibility modeling, not an empirical evaluation. While case synthesis and secondary data provide reliability, primary stakeholder interviews and on-ground deployment are proposed as future phases (discussed in Section 8). The Indo–French lens is uniquely suited to Puducherry and may require contextual adaptation elsewhere. Figure 3.1: Construction Logic of the Frugal AI Implementation Model for Puducherry The following infographic presents the layered construction logic of the proposed Frugal AI model, showcasing how it integrates national policy frameworks, global mandates, and Indo French collaborations through grounded design approaches and localized filters. This forms the basis for the Indo–French Frugal AI Innovation Hub (IFFAIH), which anchors the practical applications discussed in subsequent sections. Source: Developed by the author based on SDGs, Amartya Sen’s Capability Approach, Rogers’ Diffusion of Innovation Theory, Digital India, and Indo– French academic and policy collaborations. Figure 3.1: Construction Logic of the Frugal AI Implementation Model for Puducherry. This visual framework integrates global ethical mandates, frugal design principles, and localized socio-political filters to establish a replicable and 65 scalable innovation hub suitable for resource-constrained contexts like Puducherry. 4: Case Studies of Frugal AI in Action To validate the conceptual grounding of the Frugal AI model proposed in this chapter, this section analyzes real-world examples from India and other countries in the Global South. Each case illustrates how low-cost, scalable, and contextsensitive digital tools have been used to overcome infrastructural, linguistic, or economic limitations in delivering public services. 4.1 India: eSanjeevani National Telemedicine Platform Launched by the Ministry of Health and Family Welfare, eSanjeevani is a lowbandwidth, browser-based teleconsultation platform that supports over 80 million consultations. Built on open-source code, it operates efficiently in semi-urban and rural settings without needing high-end infrastructure. This exemplifies Frugal AI in healthcare, where diagnostic protocols are semi-automated and multilingual scripts enable wider reach. 4.2 Bangladesh: AI for Flood Forecasting In collaboration with Google and local agencies, Bangladesh developed a frugal AI model for real-time flood alerts using satellite data and machine learning. The system, accessible via basic SMS, helped reduce evacuation response time by over 60%. This case illustrates AI for climate resilience using minimal tech layers and contextual design. 4.3 Rwanda: Maternal Health AI Chatbots Through the Smart Africa initiative, Rwanda deployed multilingual AI chatbots to assist expectant mothers with prenatal information. The bots operate on feature phones using voice/text hybrids in local languages. This aligns with the Capability Approach by expanding agency and knowledge, especially in digitally underserved communities. 4.4 Tamil Nadu: DIKSHA Platform in Vernacular EdTech Tamil Nadu’s adaptation of the national DIKSHA platform localized open educational resources in Tamil for government school teachers. The platform uses AI-based text-to-speech tools and performance dashboards, enabling frugal personalization of content and tracking without major server investment. 4.5 Puducherry: Alliance Française Digital Co-learning Hubs Though not yet fully integrated with AI, the Alliance Française’s language colearning model in Puducherry offers a foundation for developing bilingual, crosscultural AI training environments. This highlights the potential for Franco Indian digital co-innovation, where frugal pedagogical tools could be enhanced by AIdriven personalization. Synthesis These case studies underscore how Frugal AI principles have been successfully applied across sectors often in geographies with sociolinguistic complexity and infrastructure constraints similar to Puducherry. They inform the implementation 66 logic of the Indo–French Frugal AI Innovation Hub (IFFAIH) discussed in the following sections. To further consolidate the relevance and replicability of frugal AI strategies across different socio-economic and cultural settings, the following table offers a comparative summary of notable case studies from India and other countries in the Global South. Each project demonstrates distinct aspects of frugal AI design and implementation—such as cost-efficiency, minimal compute dependency, local language integration, or partnership-driven scaling. Their comparative analysis not only validates the foundational principles proposed in this study but also highlights specific insights that can be adapted to Puducherry’s administrative, cultural, and geographic context. Table 4.1: Comparative Summary of Frugal AI Case Studies Country/Region Project Name Sector Frugal AI Feature Key Relevance to Puducherry India eSanjeevani Healthcare Low-bandwidth teleconsultation; open-source protocols Applicable to PHCs in rural Karaikal and Yanam Bangladesh AI Flood Alerts Climate Resilience SMS-based AI forecasting; minimal infrastructure Disaster preparedness for coastal flooding Rwanda Maternal Health Chatbots Maternal Health Voice/textbased chatbots on feature phones Maternal care outreach in multilingual villages Tamil Nadu DIKSHA Vernacular EdTech Education AI TTS tools; low-cost performance dashboards Enhances government school digital delivery Puducherry Alliance Française Digital Hubs Bilingual Learning Potential for AI co-creation in bilingual settings Foundation for Indo– French coinnovation model Source: Compiled by the author from public sector case reports, academic publications, and Smart Africa, DIKSHA, and Alliance Française resources. 67 5: Implementation Model and Design Features Drawing from the preceding literature, case analyses, and contextual mapping, this section presents the Indo–French Frugal AI Innovation Hub (IFFAIH) as a scalable and policy-aligned implementation model tailored for the Union Territory of Puducherry. The model is rooted in frugaldesign philosophy, guided by ethical AI principles, and supported through bilateral cooperation between India and France. 5.1 Pillars of the IFFAIH Implementation Model The IFFAIH model is structured around five interdependent pillars: Table 5.2: Core Pillars of the IFFAIH Implementation Model Pillar Description 1. Policy Convergence Integrates SDG targets, Digital India, NDHM, and Indo–French MoUs 2. Frugal Design Logic Low-cost, low-compute, high-access tools; promotes open-source and modular AI 3. Cultural Localisation Multilingual (Tamil–French–English) interfaces and community-centric training 4. Institutional Integration Anchored within Puducherry colleges, PHCs, and Alliance Française networks 5. Sustainability Engine Embedded in State budgets and CSR channels; monitored via open metrics Source: Constructed by the author based on analysis of policy convergence, frugal design literature, and Indo–French cooperation frameworks. 5.2 Key Design Features 1. Bilingual Accessibility Interfaces are designed to work in Tamil and French, enhancing digital participation and promoting equity in service access. 2. Device-Agnostic Deployment Solutions are designed to function across basic smartphones, tablets, and community desktops ideal for rural or underserved pockets. 3. Modular Micro services Architecture Encourages interoperability with health, education, and governance platforms already operational under Digital India. 4. Federated Learning Principles Ensures privacy-preserving AI training on decentralized data—aligned with India’s data sovereignty goals and GDPR-like frameworks in France. 5. Public Private Academic Partnerships (PPAP) Leverages France’s AI research ecosystem (e.g., INRIA, CNRS) and India’s grassroots innovators for co-design and co-implementation. 74 advances a replicable vision of digital equity. Through targeted pilot activities, rigorous monitoring, and long-term sustainability planning, the model ensures that artificial intelligence becomes an inclusive enabler not an elite disruptor. Ultimately, the chapter argues that ethical, low-cost, and community-aligned AI is not a distant ideal. With proper institutional commitment and global-local partnerships, it is a strategy ready for immediate, scalable, and measurable deployment beginning with Puducherry, and expanding wherever inclusion is a priority. References 1. Aapti Institute. (2022).AI and data stewardship pilots. https://aapti.in 2. Alliance Française. (2024).French language and cultural network in India. https://afindia.org 3. Campus France. 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