Artificial Intelligence (AI) is reshaping the contours of business, governance, social life and law worldwide. In India, the pace of adoption—spanning from generative language models to AI-powered governance tools—has raised pressing questions about whether the existing legal architecture is adequate, and what India’s regulatory path should be. This article examines the current state of AI regulation in India, the key challenges (privacy, bias, accountability, governance), and proposes what a forward‐looking framework might look like.
The Current Regulatory Landscape in India
As of 2025, India has no dedicated, horizontal legislation that specifically regulates AI systems in a technology-agnostic way. Instead, regulatory oversight is scattered across: (i) sectoral or context-specific guidelines; (ii) general statutes such as the Digital Personal Data Protection Act, 2023 (DPDP Act); (iii) policy documents such as the national AI strategy; and (iv) advisories issued by the Ministry of Electronics & Information Technology (MeitY).
For instance, MeitY has published an advisory (March 2024) requiring certain generative AI/large–language-model tools/algorithms to obtain prior permission before deployment. The draft Digital India Act, 2023 contemplates regulation for “high‐risk AI systems” including possible “no-go areas” for consumer-facing AI applications. Sectorally, guidelines exist for the health and financial sectors.
In short: India currently favours a “light‐touch” regulatory approach—enable innovation, avoid heavy upfront regulation—but recognises the need to fill gaps in governance, accountability, ethics and transparency.
Key Legal and Policy Gaps
The absence of comprehensive AI legislation does not mean absence of issues. Several gaps merit attention:
- Definition and Scope
India lacks a standard legal definition of “AI system” or “high‐risk AI”. Without this, it is difficult to draw consistent regulatory boundaries. - Transparency, Accountability & Fairness
AI systems often operate as black‐boxes. Risks of algorithmic bias, discrimination, opaque decision-making and lack of human oversight are real. Commentators observe that current Indian law lacks enforceable obligations on fairness, transparency and accountability of AI systems. - Data Protection / Privacy
AI often relies on large datasets that include personal and public data. While the DPDP Act is a step, it is yet to come into full force and may not fully address issues of purpose limitation, data quality, and training-data bias. - Liability & Redress
Who is liable if an AI system causes harm (e.g., a health-diagnosis error, autonomous vehicle accident)? The lack of a clear liability regime for AI means victims may lack effective remedial routes. - Sectoral Fragmentation
Because regulation is sectoral (finance, health, tele‐coms) rather than horizontal, there is risk of regulatory inconsistency, uneven protection, and governance arbitrage. - Innovation vs Regulation Tension
India’s developmental priorities emphasise AI for growth (healthcare, agriculture, smart cities) but excessive precaution could stifle domestic innovation. The challenge is to strike balance.
Evolving Developments & Policy Signals
Despite the gaps, there are important recent developments signalling India’s trajectory:
A Sub-committee on AI Governance published a report (for public consultation) recommending a coordinated “whole‐of‐government” governance architecture.
The DPDP Act, 2023 has been enacted (though some provisions are yet to commence) and provides a baseline for data governance that will feed into AI regulation.
Sectoral frameworks: The banking/finance sector is poised for further regulation of AI applications (risk assessment of AI in finance).
International engagement: India is part of the Global Partnership on Artificial Intelligence (GPAI) and is benchmarking against global norms while also seeking its own path.
One recent news item captures the evolving regulatory action: India’s government has proposed rules requiring labeling of AI-generated content (deepfakes, synthetic media) with clear markers, metadata traceability, user declarations—drawing on global precedents and India’s socio-cultural risks.
What Should a Legal Framework for AI in India Look Like?
Drawing on the literature, policy debates, and India’s unique context, a robust framework for AI regulation might include the following pillars:
(i) Risk-based Approach
Rather than regulating all AI uniformly, adopt a tiered approach that distinguishes low-risk, medium-risk, and high-risk AI systems. High-risk systems (those that may affect life/rights/health/welfare) would attract stricter obligations (audit, impact assessment, human oversight). This aligns with global practice (e.g., EU’s AI Act) and is already being discussed in India.
(ii) Principles‐based Code + Sectoral Rules
A horizontal law (or part of Digital India Act) could embed core AI governance principles—transparency, explainability, fairness/non-discrimination, accountability, data quality, human oversight. Then sectoral rules flesh out context-specific obligations (finance, health, judiciary, public services). India already has policy documents emphasising these.
(iii) Data Governance & Privacy Safeguards
AI relies on data; hence regulation must ensure (a) lawful collection and processing of data, (b) dataset quality, (c) mitigation of bias, (d) rights of individuals (access, correction, deletion), and (e) transparency of training data where feasible. The DPDP Act provides a foundation but will need complementary AI-specific provisions.
(iv) Liability & Redress Mechanisms
When AI systems cause harm, users need effective avenues for remediation. The law should clarify who is liable (developer, deployer, user), specify standards of care, and require incident-reporting frameworks. Some literature suggests integrating AI incident-reporting into telecom/digital policy frameworks.
(v) Oversight & Governance
An AI regulator or oversight body (could be new or within existing structure) would monitor compliance, conduct audits, enforce penalties and facilitate public consultation. India’s draft Digital India Act suggests such a body.
(vi) Incentives for Innovation
Regulation should not inhibit innovation—especially in India’s developmental sectors (agri-tech, health-tech, public services). Thus, the law should recognise sandboxes, regulatory reliefs for experimentation, public-private partnerships, and capacity building. India’s AI strategy emphasises “AI for All”.
(vii) Adaptability & Evolution
Given AI’s rapid evolution, the regulatory framework should be technology‐agnostic, principle‐based, and adaptable rather than rigid. Periodic review, stakeholder consultation, and coordination with global norms are essential.
Legal, Ethical and Social Implications:
Constitutional and human-rights dimension: Use of AI in public services raises issues of equality (Article 14), non‐discrimination (Article 15), privacy (intrinsic to Article 21) and freedom of expression (Article 19(1)(a)). For example, if an AI system denies social welfare benefits or discriminates on algorithmic grounds, it implicates constitutional rights.
Regulatory state & technology-governance: India’s approach so far reflects the tension between enabling innovation (a developmental imperative) and safeguarding rights. Legal interns must understand this balance, especially in public-interest work, e-governance projects or policy advocacy.
Litigation & liability: As deployment grows, there will be novel legal claims involving AI—from data misuse, autonomous vehicle accidents, generative-AI copyright issues, deepfake harms to reputation. Understanding liability options (contractual, tortious, regulatory) is critical.
Translation and contracts: In legal practice environments, AI‐technology contracts (service agreements, data-sharing deals) will become frequent. Interns should pick up the vocabulary: “algorithmic transparency”, “model audit”, “adverse impact”, “explainability”, “human‐in‐loop”.
Global dimension & harmonisation: India cannot regulate in isolation. Cross‐border data flows, international AI models, global platforms (OpenAI, Google, Meta) mean Indian law must align with global norms (GPAI, EU AI Act), yet reflect India’s socio-economic context.
Access & inclusion: AI has the potential to deepen inequality (digital divide, algorithmic exclusion). Law students and interns working in public policy or human rights should consider how regulation protects vulnerable groups and ensures inclusive access.
Challenges Ahead and Strategic Questions
As India moves forward, several strategic questions need to be addressed:
Timing of comprehensive law: Many stakeholders argue that India does not yet need a full-blown AI Act; instead, existing laws amended + sectoral guidelines will suffice (for now). Others say delay risks being reactive rather than proactive.
Regulatory capacity: For sectoral oversight to work, regulatory bodies must develop technical capacity—auditing AI models, understanding machine learning, monitoring bias. Without this, regulation may be hollow.
Defining “high‐risk” systems: How will India define which AI systems warrant stricter oversight? What criteria—impact on rights, life, health, critical infrastructure? Global standards can guide but India’s social context may differ.
Balancing innovation and risk: Over‐regulation may stifle startups; under-regulation may expose society to harm. The right balance is elusive.
Transparency vs trade-secrets: Mandating transparency of AI models may conflict with commercial secrecy. Should there be a disclosure regime for training data and model logic?
Ensuring fairness and mitigating bias: How will regulators ensure that AI systems do not replicate historical biases or reinforce inequality? What auditing standards will apply?
Interplay with existing laws: How will AI regulation integrate with the DPDP Act (data), the Information Technology Act, 2000 (cyber‐law), the Digital India Act (when enacted), telecom law, consumer protection law and competition law?
Global cooperation: Should India follow the EU model (comprehensive), the US model (sectoral), or a hybrid? How to account for multinational AI providers?
Conclusion
India stands at a pivotal juncture in its technological journey. AI promises immense social and economic benefits—improved healthcare, smarter agriculture, efficient public services—but also presents serious legal, ethical and governance challenges. The absence of a dedicated AI law today reflects a cautious, enabling philosophy: one that prioritises innovation but acknowledges risks.
However, as deployment grows and AI systems become embedded in critical decisions affecting life, liberty and livelihood, the need for a robust legal framework becomes pressing. A risk-based, principle-governed, adaptive regulatory architecture that is aligned with international norms yet tailored to India’s socio-economic context will be essential.
Contributed by: Lalit (Intern)