Approach to Regulating AI in India

India's AI Regulation: A Framework Missing in Action

India's laissez-faire approach to Artificial Intelligence (AI) governance exposes troubling gaps in legal accountability and ethical oversight. While the current emphasis on development and innovation appears promising for economic growth, the absence of binding institutional safeguards risks perpetuating algorithmic bias, privacy violations, and monopolistic practices. In navigating the burgeoning AI landscape, India’s policy inertia could transform technological opportunity into a governance fiasco.

Institutional landscape: A patchwork approach without legal teeth

The governing framework for AI in India is piecemeal at best. The Information Technology Act (2000) and the Digital Personal Data Protection Act (2023) indirectly address AI-related issues, such as data privacy and security, but do not encompass AI-specific challenges like algorithmic accountability or ethical compliance. The National Strategy for Artificial Intelligence (NSAI), released by NITI Aayog in 2018, merely provides a non-binding framework, leaving critical regulatory and ethical gaps unaddressed.

Sector-specific initiatives exist—like the Responsible AI for Social Empowerment (RAISE) summit (2020)—but these events have largely functioned as platforms for dialogue rather than vehicles for enforceable policymaking. The Parliamentary Standing Committee on IT called for an AI-specific regulatory authority as early as 2021, yet the government has not acted to operationalize this recommendation. Initiatives such as MeitY's IndiaAI mission, built on seven thematic pillars, remain aspirational, with no clear legislative backing.

The argument: Innovation without regulation is dangerous

The absence of AI legislation amplifies structural challenges that the sector faces—algorithmic bias is rampant, as demonstrated by AI systems like facial recognition technologies disproportionately misidentifying individuals from marginalized communities. Moreover, technologies powered by AI, such as deepfake generators, have flourished unchecked: causing electoral interference (e.g., forged election campaign videos using AI) and fostering public misinformation ecosystems.

AI-driven automation threatens traditional industries. According to a 2023 NSSO report on employment, over 1.2 million jobs in manufacturing and BPO sectors are at risk of displacement due to industrial automation—a direct consequence of unregulated AI adoption. Furthermore, India's lax regulatory environment compounds this issue, enabling monopolistic corporations like OpenAI or Google to dominate the market, creating asymmetry in power and access to technology.

The Ministry of Electronics and Information Technology (MeitY) claims that IndiaAI will foster inclusive growth, but budget allocations reveal a gap between ambition and capacity—the program was allocated less than ₹1,000 crore in FY 2023–24, starkly inadequate in comparison to the United States’ projected $1 billion investment into public-private AI innovation ecosystems in the same period.

Institutional critique: The costs of inertia

India's approach is marred by inertia and fragmented policymaking. Instead of a comprehensive legal strategy, sectoral initiatives and mission-mode programs are used to address AI deployment. This ad-hocism neither prepares India to handle systemic technological disruptions nor equips governance institutions with adequate regulatory infrastructure.

Despite multiple parliamentary reports and recommendations—such as the 2023 Standing Committee Report—the government has failed to establish an institutional structure dedicated to algorithmic audits or risk assessments. Public consultations, which should form the backbone of an AI regulatory framework, remain cursory: stakeholders such as civil society groups and academic experts report limited outreach.

Counter-narrative: Does regulatory flexibility foster innovation?

Proponents of India’s current AI posture argue that a flexible, innovation-driven approach enhances the competitive edge in a rapidly evolving global market. They claim that early over-regulation could stifle entrepreneurship, especially in smaller startups looking to innovate without bureaucratic overhead.

While countries like the European Union have imposed stringent operational requirements in their AI Act, critics argue that India’s economic priorities demand a lighter touch, permitting growth through experimentation. However, this argument falters when examined alongside the ethical and social costs of unbridled experimentation—public distrust in AI, amplified inequality, and misuse of AI systems jeopardize long-term economic creativity.

International perspective: A lesson from the European Union

The European Union (EU) offers a pointed contrast to India’s laissez-faire governance. The EU’s Artificial Intelligence Act, expected to be operational by 2024, categorizes AI systems based on risk (e.g., high-risk, limited-risk) and mandates algorithmic transparency, fairness audits, and clearly defined liability mechanisms. This rule-based approach not only protects public safety but sets ethical benchmarks for innovation.

What India calls “development-led AI governance,” the EU calls a regulatory vacuum—an approach that risks concentrating the benefits of AI within monopolistic corporate entities. India, with its socio-economic diversity, cannot afford governance models that overlook ethical trade-offs, unlike Germany, where cooperative federalism ensures equitable AI resource allocation.

Assessment: From aspirational frameworks to enforceable governance

India urgently needs to prioritize regulatory safeguards over policymaking rhetoric. At a minimum, the government should pilot algorithmic transparency mechanisms and set up ethics committees across AI-relevant sectors (healthcare, education, agriculture). Enacting a National AI Policy that forces sectoral accountability—alongside investments in skill development—would align India’s AI growth with ethical imperatives.

Without meaningful intervention, India’s AI landscape risks descending into trust erosion, innovation capture, and social imbalance. The challenge is not simply regulatory design—it lies in the state’s capacity to enforce such frameworks across sectors, ensuring equitable access and inclusive participation.

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