India Panel Seeks Royalties, Licenses for AI Training Data

A government advisory panel in New Delhi on December 9 proposed a sweeping new framework that would require artificial intelligence companies to pay royalties into a central pool for the use of creators' content in model training, and to obtain licenses for training data. The draft report, aimed at major technology firms including OpenAI and Google, invited comments from industry, creators, and civil society, and sets out a model that contrasts with the United States notion of fair use.

The proposal represents a notable shift in how a large market is contemplating the balance between commercial AI development and creators' rights. By channeling payments through a centrally administered pool, regulators seek to standardize compensation and reduce bilateral negotiation complexity. Proponents say the system would recognize the value of text, images, audio, and video that underpin modern machine learning models and provide a revenue stream for authors, artists, and publishers who have seen their work incorporated into sprawling training datasets.

Implementing such a regime raises immediate technical and legal challenges. Determining which pieces of content were used to train a particular model and the extent of that use is not straightforward. Machine learning models are typically trained on massive, blended datasets where individual contributions are transformed into statistical patterns. Verifying provenance and measuring usage therefore require new forms of auditing and transparency that regulators and firms have yet to standardize.

The panel's licensing concept also carries economic consequences. If adopted into national law or regulation, it could compel AI companies to renegotiate commercial terms in India, a major global market with hundreds of millions of internet users. Those costs may be passed along to end users or accelerate efforts by companies to assemble licensed data or to invest more heavily in synthetic or proprietary datasets. For creators, the measure could improve bargaining power and income streams, particularly for those whose work is frequently scraped into training corpora.

Internationally the proposal could spur debates about regulatory fragmentation. The draft explicitly diverges from the United States fair use approach, which has been interpreted in ways that sometimes permit broad reuse of online content for transformative purposes. If India moves to formalize royalties and licensing, other jurisdictions may follow or respond with their own rules, complicating compliance for firms operating across borders.

Enforcement will be a critical issue. A central pool model requires mechanisms to collect payments fairly, adjudicate claims from creators, and prevent capture by intermediaries. It also needs technical tools to audit model training processes and to verify licensing. Absent clear, operational standards, the regime could generate litigation or protracted regulatory disputes.

The panel has opened a comment window to stakeholders, signaling that the draft remains subject to revision. The debate now shifts to a wider public consultation where creators, technology companies, academic researchers, and civil society groups will assess whether royalties and licensing can be administered in a way that preserves innovation while ensuring fair compensation for those whose work fuels artificial intelligence. The answer will shape not only the Indian market, but potentially the global architecture for data and AI governance.