[Answered] Examine India’s ‘Third Way’ for AI governance as an alternative to the market-led and regulation-heavy global models. Evaluate how this development-centric approach balances technological sovereignty with the institutional needs of the Global South.

Introduction

According to the IMF (2024), AI could affect 40% of global jobs, while UNCTAD flags concentration of AI compute within a few firms. Amid this asymmetry, India’s ‘Third Way’ proposes inclusive, sovereignty-driven AI governance.

Moving Beyond Binary Models: Market Fundamentalism vs Regulatory Maximalism

1. Market-Led Model (U.S. Approach): Innovation First: The U.S. relies largely on ex-post regulation and private-sector leadership (e.g., OpenAI, Google DeepMind). While this accelerates frontier innovation, it risks regulatory lag, algorithmic opacity, and labour displacement without social safeguards.
2. Compliance-Heavy Regime (EU AI Act): Precautionary Governance: The European Union’s European Union AI Act adopts a risk-tier classification (minimal to unacceptable risk), embedding ex-ante conformity assessments. Though rights-protective, its high compliance costs may burden developing economies with limited regulatory capacity.
3. State-Centric Model (China): Centralised Algorithmic Control: China’s approach integrates AI within state planning and cybersecurity laws, prioritising data localisation and algorithm registration. While ensuring control, it may constrain innovation pluralism and global interoperability. India’s Third Way emerges as a synthesis—innovation-enabling yet norm-anchored, avoiding both laissez-faire excess and regulatory overreach.

Development-Centric Governance: AI as Digital Public Infrastructure (DPI)

1. AI for Public Goods Delivery: India conceptualises AI as an extension of Digital Public Infrastructure (DPI), akin to Aadhaar and UPI. Through the IndiaAI Mission and sectoral guidelines, AI deployment is targeted at healthcare diagnostics, agricultural advisories, and education personalisation—aligning with SDG commitments.
2. Sector-Specific, Agile Regulation: Instead of omnibus legislation, India works through existing frameworks such as the Digital Personal Data Protection Act, 2023 and amendments to IT Rules mandating AI-generated content labelling. This reflects adaptive governance rather than static codification.
3. Sandboxes and Voluntary Codes: Regulatory sandboxes encourage experimentation while embedding accountable-by-design principles. This mirrors the RBI fintech sandbox model, balancing innovation and oversight.

Technological Sovereignty: Strategic Autonomy in the Algorithmic Age

1. Data Sovereignty and Indigenous Models: India promotes indigenous large language models such as BharatGen to preserve linguistic and cultural intelligence. This reduces algorithmic dependency and mitigates vendor lock-in risks flagged by the World Bank’s Digital Development Report (2023).
2. Compute Infrastructure Democratization: Through subsidised GPU access under the IndiaAI Mission, India attempts to counter compute colonialism, where AI power is concentrated among a handful of Global North corporations.
3. Public-Private Partnerships (PPP Model): Unlike statist centralisation, India leverages PPPs across the AI value chain—research labs, startups, and academia—enhancing institutional scalability without fiscal overextension.

Institutional Relevance for the Global South

1. Context-Sensitive Governance: Many Global South states lack regulatory bandwidth for 400-page statutes like the EU AI Act. India’s principle-based, modular framework—Trust, Fairness, Human Oversight—offers replicability without heavy compliance infrastructure.
2. Capacity-Building Diplomacy: By hosting AI Impact Summits and advocating shared safety evaluation frameworks, India positions itself as a convenor among middle powers—bridging innovation asymmetries identified by UNDP.
3. Inclusion and Multilingual AI: Initiatives like Bhashini address linguistic marginalisation, making AI diffusion socially embedded rather than elite-centric—a crucial requirement for equitable technological transformation.

Critical Gaps and Normative Challenges

1. Labour Displacement and Just Transition: The ILO warns of automation-led employment disruption. India’s framework must integrate skilling, social protection, and algorithmic impact assessments to avoid developmental dualism.
2. Accountability and Enforcement Deficit: Voluntary codes may lack teeth without statutory backing. Ensuring algorithmic auditability and grievance redress mechanisms remains vital.
3. Global Coordination Imperative: AI harms transcend borders; hence India’s sovereignty model must operate within multilateral norms to prevent regulatory fragmentation.

Conclusion

As President Dr. A.P.J. Abdul Kalam emphasised in India 2020, technological power must serve national development. India’s ‘Third Way’ embodies this ethos—strategic autonomy fused with inclusive, globally responsible innovation.