India’s ‘Third Way’ Redefining Global AI Governance

India’s ‘Third Way’: Bold AI Vision or Risky Shortcut?

India’s ambitious ‘Third Way’ in AI governance—a localized model that balances innovation, social equity, and regulatory pragmatism—could redefine global norms. But the lack of institutional rigor, fragmented laws, and an over-reliance on strategic optimism pose profound risks. Without systematic safeguards, India may merely replicate the inequities of established AI powers, not resolve them.

The Institutional Landscape of AI Governance

India’s governance effort in AI deploys existing legal frameworks like the Information Technology Act, 2000 and the Digital Personal Data Protection Act, 2023 rather than new, standalone legislation. Recent amendments to intermediary rules now mandate AI-generated content labeling—one of the first such measures globally. While promising, India’s strategy is both structurally ambitious: focusing on AI contributions to healthcare, agriculture, education, and public administration; and structurally ambiguous: bypassing specialized AI legislation in favor of patchwork updates to digital governance laws.

Contrast this with the rigid provisions of the European Union’s AI Act, which classifies AI systems by risk and burdens innovation with heavy compliance demands. Meanwhile, the U.S. has embraced its laissez-faire approach, betting on market-driven innovation tempered by sectoral guidelines. China enforces centralized control over data, algorithms, and AI companies—cementing state supremacy in dictating outcomes. India’s model, dubbed the ‘Third Way,’ seeks to operate somewhere between these extremes, capitalizing on its digital public infrastructure achievements such as Aadhaar, UPI, and DigiLocker. However, these ambitions rest precariously on limited capacities.

Argument: Building India’s Case with Evidence

India’s leap into national AI governance includes pioneering rules such as a three-hour takedown mandate for harmful AI-generated content, coupled with transparency requirements—a global first. However, take-down mandates strain enforcement capabilities amidst resource deficits. In 2023, the Digital Personal Data Protection Act was introduced to address data-driven risks. But its broad state exemptions undermine confidence in any AI-reliant surveillance oversight.

Worker displacement challenges further expose gaps. Estimates by the International Labour Organization (ILO) clock automation risks at 55% across middle-skilled jobs worldwide, potentially impacting India’s IT and gig economies disproportionately. Yet, India has no reskilling frameworks at scale—a glaring omission in a ‘Third Way’ allegedly centered on equity.

Essential infrastructure gaps exacerbate risks. India still depends on foreign cloud providers like Amazon Web Services and imported semiconductors—a dependency highlighted by the Ministry of Electronics’ delayed implementation of its $10 billion production-linked semiconductor subsidy scheme launched in 2021. Lacking access to high-performance computing also limits India’s strategic autonomy in global AI governance discussions.

Adding socio-cultural complexity, India’s AI systems face bias risks spanning linguistic diversity (22+ official languages), caste inequities, and algorithm profiling blind spots. The lack of algorithms tailored to India’s pluralistic realities may amplify discriminatory outcomes, especially given that most foundational AI language models integrate predominantly Western datasets.

The Counter-Narrative: Strengths, but Can It Scale?

Proponents argue India’s AI governance approach complements Global South policy needs, offering solutions that bypass Global North reliance while enabling broader participation. India’s AI Innovation Mission, operational since 2024, has reportedly incubated over 150 AI startups across agriculture and healthcare. Public-private collaborations—such as partnerships under the India–AI Global Partnership—signal room for consensus-driven frameworks outside established AI hegemonies.

However, scaling such consensus frameworks domestically through reliance on fragmented legislation risks failure. The absence of a standalone AI regulatory body comparable to the European Commission’s High-Level Expert Group on AI limits India’s ability to institutionalize risk monitoring comprehensively. Moreover, enforcement mechanisms against global tech giants—like Meta and OpenAI—remain prohibitively weak without cross-border legal harmonization.

Lessons from Comparative Global Models

Germany, under the EU’s AI Act, applies resource-intensive risk-compliance audits tailored to safeguard end-user rights. While innovation slows, standards in ethical AI deployment tighten considerably. What India dubs “structural pragmatic AI governance,” Germany might critique as “false economy,” sacrificing risk auditing for short-term adoption acceleration.

Germany’s AI ecosystem benefits from computing infrastructure (Deutsches Elektronen-Synchrotron DESY) and subsidies for independent AI research institutions. India’s dependence on proprietary AI systems like GPT-4 contrasts starkly, undercutting its bargaining power in negotiating foundational model adaptations suited to Indian development goals.

Assessment: Policy Adjustment or Risk Escalation?

The strengths of India’s ‘Third Way’ remain unmatched in the Global South for ambition and inclusivity. Public infrastructure programs like DigiYatra and workforce-targeted AI skilling initiatives could build socio-economic resilience against automation. However, weak enforcement mechanisms, fragmented global coordination, and thin regulatory authority leave India vulnerable to exploitation by Global North tech monopolies.

The AI Impact Summit may bring glimmers of multilateral cooperation among middle powers. Key questions remain unaddressed, challenging stakeholders to deliver Indian AI regulations that are equitable and enforceable—not aspirational and porous. A standalone AI law coupled with a Global South AI alliance framework could mark the policy evolution India urgently requires.

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