Migration & India’s Languages — A Complex Relationship of Loss and Innovation

India is one of the world’s most linguistically rich countries—122 major languages and 1,600+ dialects weave together our cultural fabric. But as rural–urban migration, interstate mobility, and seasonal labour flows accelerate, the linguistic landscape is being reshaped in profound ways.

1. The paradox: Migration can enrich languages through mixing (think Hinglish or Marathi–Konkani blends) while also eroding mother tongues when communities disperse or when children don’t get early literacy in their heritage languages. The outcome depends on who migrates, where, and how services respond.

This blog post brings together the risks, the data gaps, the technology landscape, and a practical policy + product playbook to keep India’s linguistic diversity alive - not just in homes and schools, but inside our apps, helplines, and digital public infrastructure.

2. What’s Changing on the Ground:

• Heritage language loss among migrant children: Many children from tribal and migrant families are not acquiring literacy or fluency in languages like Kui, Kuvi, Bhatri, Santali, Gondi, and others.
• Data deserts in AI: Current ASR/NLP datasets under-represent migrant dialects and tribal speech. This makes speech tech brittle in the very contexts where it’s most needed.
• Digital service gaps: Voice-first public platforms - helplines, skilling apps, agristack services - struggle to serve migrant populations because the language variety they encounter isn’t well-supported.

3. Bright spots: 

• Project Vaani (IISc + ARTPARK + Google): One of the largest Indian speech datasets ever created—targeting 150,000+ hours of audio from every district. Phase 1 already collected 14,000 hours across 80 districts.
• Bhashini: India’s national language translation mission, enabling multilingual public services.
• Bhashadaan: A crowdsourcing initiative that invites citizens to donate voice samples.
• IndicCorp, Whisper-based pipelines, and AI4Bharat projects: Documenting endangered dialects and building robust multilingual ASR models.

4. Policy Moves to Strengthen Linguistic Inclusion

4.1 Strengthen Mother Tongue Education for Migrant Children: Introduce bridge language programs in govt. schools (Grade 1–3).  Deploy community-taught classes in tribal languages under Samagra Shiksha. Expand SCERT’s Mother-Tongue Based Multilingual Education (MTB-MLE) to urban migrant clusters. Policies like NEP 2020 promote multilingual education, but implementation gaps in migrant communities hinder mother tongue retention.

4.2 Establish Urban Language Support Centres: Create Language Inclusion Cells in municipal schools, ICDS centres, and skill centres. Provide translation and interpretation support for: Health workers, Social protection schemes and Welfare enrolment (PM-KISAN, MGNREGS, PDS)

4.3 Invest in Tribal and Migrant Language Digitization: Collect speech datasets in Kui, Kuvi, Gadaba, Bhatri, Bhojpuri, Santhali, and regional dialects. Partner with ARTPARK, AI4Bharat, IIIT-H, IIT Madras, and local universities. Use voice-first interfaces for public-facing govt. apps.

4.4 Integrate Linguistic Diversity into Digital Public Infrastructure: Ensure DPI platforms (Bhashini, Agristack, UHI, ONDC) support migrant/mother tongue language packs. Deploy offline voice-to-text tools for low-connectivity migrant populations.

4.5 Community-Led Preservation Initiatives: Establish cultural documentation hubs in tribal migrant communities. Use community radio, YouTube, WhatsApp micro-learning, and storytelling apps to strengthen language retention.

4.6 Incentivize Research & Innovation: Create grants for universities and NGOs to build language maps, dictionaries, and oral corpora. Support technology innovators building low-resource language ASR models.

The Bottom Line

Migration isn’t the threat—exclusion is. Languages disappear when communities move but institutions don’t adapt. India has the talent, infrastructure, and public digital platforms needed to preserve its linguistic diversity. With the right investments, schools, apps, datasets, and public services can fully reflect—and celebrate—the languages people actually speak.

Artifical Intelligence (AI) for Inclusive Societal Development - Viksit Bharat 2047

NITI Aayog on October 8 released a pioneering study, AI for Inclusive Societal Development. The roadmap proposes a national mission "Digital ShramSetu" that leverages AI and frontier technologies to overcome systemic barriers faced by informal workers and can be harnessed to transform the lives and livelihoods of India’s informal workers. The five key components of roadmap:

1. Develop a national blueprint
2. Coordinate fragmented stakeholders
3. Catalyse strategic partnerships
4. Translate innovation into impact
5. Provide policy and regulatory support

Ecosystem 

India has one of the largest informal economies in the world, with about 90% of the workforce employed under informal arrangements, contributing nearly half (around 45-50%) of the country's GDP having 490 million informal workers. The informal sector includes unregistered enterprises, self-employed workers, casual laborers, domestic workers, and informal service providers, often lacking social security benefits. India's e-Shram portal, launched in August 2021 to create a National Database of Unorganised Workers (NDUW), has registered over 30.98 crore unorganised workers as of August 2025.

Migration and urban informal work are intertwined, with informal jobs. The informal sector poses challenges like poor working conditions, job insecurity, and exploitation, especially for migrant workers. Indian MSMEs employing informal workers also suffer on a competitive scale is the quality of talent. Businesses compensate for inferior quality labour with depressed wages which in turn creates an unattractive career pathway; hinders upward mobility; and disincentivizes talent.

Additional Reading: Why do rural enterprises remain small ?

Challenges

1. Harassment of MSMEs by labour inspectors is a reported issue in India, reflecting concerns over misuse of power, frequent inspections, and arbitrary penalties. The complex regulatory environment and multiple overlapping laws cause delays and create opportunities for rent-seeking behaviors from officials.

2. Workers with limited digital literacy become more dependent on intermediaries (officials, cybercafe operators, CSC operators) who can extract rents. This will create new rent-seeking opportunities. Local officials could charge fees for "faster processing" of digital IDs or demand bribes

3.Bureaucrats resist change, preferring to maintain their power and scope. Incentives encourage expanding departments and budgets rather than achieving efficiency. The administrative state centralizes power among unelected officials. The same bureaucrats who struggle with existing schemes will be tasked with implementing AI-powered verifiable credentials and smart contracts. 

4. Drawing from James C. Scott’s work, the discussion delves into how increased state legibility—enabled by systems like Aadhaar and UPI— have enabled government to operate from 2009 to 2024 without Privacy Law. The 15-year gap since Aadhaar’s launch without a privacy framework underscores systemic neglect. India currently lacks a fully enacted constitutional act specifically dedicated to AI regulation akin to the European AI Act.

5. Even if there is motivation in the government at top tiers, there is not always capacity to understand complex technological systems by frontline user.  India's DPI success (UPI, Aadhaar) succeeded because they involved standardized, high-volume transactions with limited discretionary implementation. Digital ShramSetu requires complex, discretionary decision-making at the local level—exactly where Indian state capacity is weakest and most corrupt.

6. Like poverty status, the classification of workers as formal or informal is fluid. Workers may shift between informal and formal employment due to job transitions, gig economy roles, and contractual changes. This fluidity complicates policy design, social protection coverage, and statistical measurements, demanding adaptive, inclusive frameworks.

7. India's skill development ecosystem reveals a systematic corruption pattern that AI implementation could either amplify or mitigate, depending on design choices.

Suggestions

1. AI algorithms can be used to match registered workers with job opportunities in their skill areas and geographic locations, optimizing employment pathways and reducing informality and underemployment. This can be initiated from Polytechniques and ITIs in the initial phase and gradually used for unorganized workers.

2. e-Shram portal must provide AI-facilitated interoperability with other government benefits like UDYAM, e-Pension, post office and healthcare schemes can offer a seamless experience for workers, facilitating holistic social protection. 

3. When an informal worker registered on e-Shram secures formal sector employment, their verified credentials and employment history can be linked to EPF enrollment processes, helping with identity verification, tracking contributions, and ensuring portability of social security benefits.

4. The roadmap assumes informal workers want to transition to formal systems. Application of the technology must necessarily be accompanied by design of transparent processes.  AI can be used for self-certification, digitization of compliance to reduce physical inspections, and stronger grievance redressal mechanisms to protect MSMEs from excessive or unfair enforcement. This is important to create pathways for the informal worker to initiate the journey into an entrepreneur integrated into formal economy. 

5. Labour courts and dispute resolution mechanisms are increasingly exploring the use of AI to improve efficiency, reduce backlogs, and enhance fairness in labour law enforcement. AI can analyze large volumes of workplace cases, assess precedents, and suggest outcomes based on legal principles, helping resolve disputes like wrongful termination more systematically.

6. Rather than voluntary adoption, India can consider sector-by-sector mandatory digitization starting with high-impact areas like contractual workers of PSUs and PM Vishwakarma beneficiaries. 

7. Last but not least, India must separate policymaking, implementation, and oversight functions.  There must be creation of an independent ombudsman systems for digital services and platform involved in gig economy. 

8. The mission should operate in true mission mode: establishing autonomous implementation units at state level with direct resource allocation, hiring authority, and performance accountability, bypassing traditional bureaucratic hierarchies that create implementation bottlenecks.

Global Lessons

Estonia’s government ministries are required to appoint AI officers and create AI implementation plans, effectively making AI adoption in public sector organizations a regulated requirement. In summary, Estonia mandates AI adoption and implementation plan within defined sectors such as education and government administration. Yet, Estonia's digital success required complete administrative restructuring before technology deployment.

Inside Amsterdam’s high-stakes experiment to create fair welfare AI: Even though Netherland Government worked hard to build a fair AI system to detect welfare fraud, the algorithm still showed bias against people with non-Dutch speaking migrants and those with lower incomes. Ethical AI needs ongoing human oversight, community involvement, and understanding that automation has limits when dealing with complex social fairness issue.

Conclusion

India's Digital ShramSetu mission confronts a fundamental paradox: it requires sophisticated state capacity to implement solutions for populations that exist precisely because of weak state capacity. India's Digital ShramSetu mission could indeed be transformative, but success requires acknowledging current limitations rather than assuming technological solutions will overcome social and economic realities. The Digital ShramSetu mission's success depends on recognizing that technology is a governance multiplier, not a governance substitute

Building Inclusive Digital Futures: The Role of Digital Public Goods and Infrastructure

Here is a blog post based on the learnings of Digital Public Infrastructure(DPI) and Good(DPG) for Impact with a special focus on India's digital ecosystem and global perspectives. 

The digital transformation of societies and economies hinges increasingly on foundational systems that provide open, trusted, and interoperable digital infrastructures. Digital Public Goods (DPGs) and Digital Public Infrastructure (DPI) are emerging as critical enablers of inclusive growth, transparency, and innovation at scale. This blog dives deep into how these concepts are shaping India’s digital landscape and what lessons the world can learn from India’s pioneering efforts.

The rapid growth of Digital Public Infrastructures (DPIs) and digital platforms is driven by a convergence of policy shifts, technological evolution, and societal demands. At the same time, data-based governance has become central to policymaking, with real-time analytics enabling targeted welfare delivery, fraud prevention, and performance monitoring. These forces, combined with advances in cloud, open-source software, and API-driven architectures, are creating a virtuous cycle of adoption where DPIs and digital platforms are not just tools, but foundational enablers of inclusive, transparent, and efficient service ecosystems.

What Are Digital Public Goods and Infrastructure?

Digital Public Goods (DPGs) are open-source software, data, standards, and AI models that are freely available for anyone to use, adapt, and scale. They serve as building blocks for creating digital services that are inclusive and scalable globally. Examples include India’s Aadhaar biometric identity system, UPI payment platform, and open protocols like Beckn for commerce.

Digital Public Infrastructure (DPI) refers to large-scale, interoperable digital platforms built on foundational DPGs that enable ecosystems of public and private actors to deliver services. DPI represents the "railways" or highways of the digital economy—open, shareable, secure, and enabling many-to-many interactions. India Stack, which powers Aadhaar, UPI, DigiLocker, and Account Aggregator frameworks, is a prime example.

India’s Digital Leadership: The India Stack and Beyond

India Stack integrates several layers of DPI, each designed to solve key challenges of identity, payments, data exchange, and commerce:

These layers underpin numerous government and private sector services, creating a robust digital ecosystem promoting financial inclusion, transparency, and new business opportunities.

Emerging Innovations: AI & Language Technology

New digital layers harness AI and natural language processing to serve India’s digitally underserved populations:

1. BHASHINI (Bhasha Interface for India): A multilingual AI-powered language platform offering translation, speech recognition, and voice-enabled digital services across 22 Indian languages, breaking down language barriers and enabling greater digital participation.
2. AI-driven Personalization and Fraud Detection: Embedded in services across healthcare, financial inclusion, and governance, AI models enable predictive analytics, user-tailored experiences, and automated compliance, enhancing service quality and security.

Global Perspectives on Digital Public Infrastructure

Digital IDs generally fall into two categories — foundational and functional — and different countries implement them according to their governance and service delivery priorities.

Foundational digital IDs serve as universal, multipurpose identifiers that legally establish an individual’s identity and enable access to a broad spectrum of services such as banking, healthcare, voting, and welfare. Their primary purpose is to act as the central proof of identity recognized across multiple sectors. Notable examples include India’s Aadhaar, which combines biometric and demographic data to facilitate services like e-KYC and subsidies.

Functional digital IDs are sector-specific and designed to verify eligibility or access within a particular domain rather than serving as a universal identity. They function within defined service areas and often rely on foundational IDs for authentication. Examples of functional IDs include India’s ration card and voter ID, which are primarily used for food subsidies and electoral processes, respectively.

These initiatives underline the global recognition that public digital infrastructure is foundational to modern governance and economic development.

Benefit of Digital Public Goods and Infrastructure

DPG is a public, private, and government read, which means that public citizens and everybody else also participate into building it, maintaining it, and enriching it. And private players make use cases, make business cases out of it, make money out of it, and help to translate those government benefits to the citizens and in turn, making a win-win situation for everyone. As the cost of acquisition goes down in digital mode, and the moment the cost of acquisition goes down, the cost of serving becomes easier for the private companies, for government, and also for citizens to access those services.