In light of the various copyright disputes concerning AI firms, Tirthaj Mishra argues that India should shift the burden of licensing for AI training data from creators to AI companies. The guest post critiques the ineffectiveness of the opt-out model using robots.txt and proposes a statutory \u201cDuty to License\u201d framework inspired by India\u2019s broadcasting laws under Section 31D of the Copyright Act. Tirthaj is a 3rd year law student at Maharashtra National Law University Mumbai. His academic focus centers on the intersection of technology and legal frameworks, particularly in fields like artificial intelligence and intellectual property.<\/p>\n By Tirthaj Mishra<\/em><\/p>\n India\u2019s generative AI industry relies on creative works for training their models, yet creators bear the burden of protecting IP through\u00a0robots.txt<\/a>, a protocol lacking enforceability and disproportionately impacting creators as it\u2019s their responsibility to manually\u00a0opt-out<\/a>\u00a0of the dataset.\u00a0\u00a0Additionally, this archaic system can only block entire domains, not individual works limiting their protection, and is frequently ignored by AI crawlers.\u00a0<\/p>\n This post advocates for imposing statutory licensing obligations on AI firms,\u00a0inspired by India\u2019s broadcasting laws under section 31D, to achieve a fair balance between fostering innovation and safeguarding creator rights in the Indian generative AI market. The\u00a0robots.txt protocol<\/a>\u00a0is a text file through which website owners can indicate the directories or pages they do not want web crawlers to visit. robots.txt directives have no technical or legal enforcement powers and are therefore strictly advisory. This voluntary model of compliance introduces a very uneven playing field for creators and leaves data vulnerable to extraction by bad-faith actors without the protection of a legal safeguard while depending on outdated preventive methods which lack enforceability.\u00a0<\/p>\n Domain-blocking doesn\u2019t work on third-party mirrors\/archives as robots.txt depends on voluntary compliance and is thus limited in effect. Major search engines obey it but third-party operations and other crawlers often disregard these limitations. E.g.,\u00a0the Wayback Machine<\/a>. It is also not effective in shielding individual content on websites like articles and images because it only applies to entire domains or subdirectories as a result, individual works are left vulnerable.<\/p>\n Section 31D of India\u2019s Copyright Act<\/a>\u00a0allows broadcasters to use copyrighted works without prior approval by paying royalties to rights holders. This provides public access to content while fairly compensating creators. Section 31 D mandates broadcasters to notify copyright owners and pay royalties set by a tribunal or court. This ensures fair compensation and prevents hoarding and monopolising of works.\u00a0<\/p>\n While Section 31D currently applies to literary\/musical works and sound recordings, its principles can be extended to all copyright categories-from software code to visual art-through amendments. However, the\u00a0Wynk v. Tips<\/a>\u00a0litigation highlights risks: courts may reject analogies between broadcasting and AI training without explicit legislative intent. To avoid this, India must redefine \u2018communication to the public\u2019 to include algorithmic intake and establish tiered royalties, ensuring startups and giants alike contribute fairly.<\/p>\n This statutory licensing framework can regulate the use of data by AI companies for training AI models by imposing a statutory duty on the companies to license training data. This framework shifts the responsibility of compliance from creators to AI companies, similar to how broadcasters must license music, such as: fixed minimal fees for startups (\u20b91,000\/10k works) and revenue-sharing for giants (2% of AI-related income). The\u00a0UK\u2019s CLA<\/a>\u00a0and\u00a0Israel\u2019s tiered systems<\/a>\u00a0prove this balances accessibility and fairness.\u00a0An\u00a0AI Training Registry<\/a>-a public blockchain ledger-can automate tracking, avoiding the manual audits that plagued radio licensing\u201d Payment models can be made flexible and not confined to per-usage royalties but also other mediums such as revenue-sharing, ensuring fair compensation for extensive data use. The reasoning behind this framework is to establish parity between broadcasters and AI firms: just as broadcasters must license content for public benefit, AI companies should license training data to prevent exploitation and ensure equal access to innovation.\u00a0<\/p>\n To regulate uncontrolled data scraping, India\u00a0can\u00a0adopt a statutory\u00a0\u201cDuty to License\u201d<\/strong>\u00a0framework, compelling AI firms to\u00a0actively\u00a0license training data.\u00a0The\u00a0proposal\u00a0is based on a\u00a0Three-Pillar Framework<\/strong>\u00a0to balance both innovation\u00a0and\u00a0creators\u2019\u00a0rights.<\/p>\n AI firms would be required to maintain\u00a0mandatory training data manifests<\/strong>, keeping a record of the data\u00a0on\u00a0which training\u00a0takes place.\u00a0The\u00a0manifests would\u00a0be\u00a0made\u00a0available to the public\u00a0and\u00a0bring\u00a0accountability, enabling creators to track\u00a0whether\u00a0their works were\u00a0used.\u00a0For\u00a0this, India\u00a0can\u00a0establish an Indian AI Training Registry, a\u00a0public\u00a0database where firms\u00a0submit\u00a0usage. This registry would\u00a0be\u00a0a public record,\u00a0raising\u00a0trust and\u00a0slowing\u00a0controversies\u00a0over unauthorized use. (For more on this, see\u00a0here<\/a>).\u00a0<\/p>\n To address problems like huge datasets and data provenance multiple options exist such as:\u00a0<\/p>\n Automated Content Fingerprinting:<\/strong> Tools like the\u00a0Data Provenance Initiative\u2019s<\/a>\u00a0hashing algorithms can generate unique identifiers for creative works, enabling automated cross-referencing against training manifests. For example,\u00a0DECORAIT\u2019s decentralized ledger<\/a>\u00a0uses cryptographic hashes to track consent, allowing creators to register works\u00a0before<\/em>\u00a0training begins.<\/p>\n Opt-In Defaults with Smart Contracts:<\/strong> By integrating standardized opt-in\/out protocols (e.g.,\u00a0C2PA<\/a>) into the registry, creators could pre-emptively license works under terms that trigger automated payments via smart contracts when matches are detected. This shifts the\u00a0tracking burden to AI firms, not creators.<\/p>\n Collective Licensing via Copyright Societies:<\/strong> India\u2019s existing copyright societies (e.g., IPRS for music) could manage bulk licensing and auditing, leveraging their infrastructure to identify infringements. The EU\u2019s\u00a0DSM Directive<\/a>\u00a0shows collective management reduces individual monitoring burdens. (related<\/a>)<\/p>\n Hybrid Human-AI Audits:<\/strong> While AI firms use algorithms to curate data, the registry could mandate periodic third-party audits using tools like\u00a0OLMoTrace<\/a>, which links model outputs to training sources. Creators would still need to verify usage, but automated fingerprinting and collective management minimize manual effort.\u00a0<\/p>\n While imperfect, this system is a\u00a0critical improvement\u00a0from robots.txt, shifting the compliance burden\u00a0from creators to firms\u00a0and enabling better redressal.\u00a0<\/p>\n Critics ignore factual information from the broadcasting industry in India, whose growth was accelerated by statutory licensing under Section 31D. After the 2012 reforms were put into effect, sectoral revenue increased by 23%, allowing over 300 radio stations to continue operating while providing appropriate remuneration (see here<\/a>). Broadcasters could prioritize content diversification rather than worrying about drawn-out discussions through organized structures (related<\/a>). Stakeholder interests are best served by legal clarity, which has also promoted innovation in small\u00a0digital platforms and regional language programmes. The Madras High Court dismissed arguments claiming compliance stifles innovation by upholding the constitutionality of Section 31D and highlighting its function in striking a balance between creators rights and open access (related<\/a>).<\/p>\n While broadcasting and AI differ in technical execution, the constitutional balancing of rights and access under\u00a0Section 31D\u00a0applies universally.<\/p>\n AI leverages creative works through algorithms, not by directly sharing them with the public like broadcasting does. Yet, it still profits from creators\u2019 efforts, as seen in\u00a0Andersen v. Stability AI<\/a>. Statutory licensing ensures creators are paid for this use.<\/p>\n Hybrid payment systems\u2014like small fixed fees for startups and revenue-sharing for big firms\u2014promote fairness and access, as explained\u00a0here<\/a>.<\/p>\n Following statutory licensing model protects AI firms from infringement lawsuits, as shown in\u00a0Phonographic Performance Ltd v. ENIL<\/a>.<\/p>\n Extending these principles to AI helps India boost innovation equitably while dodging U.S.-style legal battles.<\/p>\n The U.S. Copyright Office-tested solution for dealing with orphan works is a mandatory licensing pool with escrowed fees, which temporarily holds royalties until rights holders come forth (US report<\/a>).\u00a0Section 33<\/a>\u00a0of Indian Copyright Act already empowers the copyright societies to collectively manage royalties-income that include orphan works, and this does not require amendment. This model is also similar to the EU orphan works framework as it avoids red tape but doesn\u2019t lose incentives for rights holders (related<\/a>). With this framework, India can bring itself to the global standard and get fair remuneration while lessening infringement risks.<\/p>\n Opt-out regimes systematically disadvantage marginalized creators, only 12% of non-English content creators in India make use of opt-out mechanisms, compared to 68% of English-language creators as seen in (KPMG report<\/a>)(Ipsos survey<\/a>)(also see related<\/a>). This significant disparity suggests that non-English creators have less awareness of, or access to, digital rights protections. As a result, they are less able to control how their content is used, especially in areas such as AI training and digital marketing.\u00a0<\/p>\n The EU\u2019s AI Act<\/a> proves ethical frameworks draw capital from investors when paired with legal certainty (read more about EU\u2019s AI act here<\/a>\u00a0and\u00a0here<\/a>). While Section 84\u2019s\u00a0pharma licensing faced investor skepticism\u00a0<\/a>, AI licensing under Section 31D is different as it ensures reciprocity: firms pay royalties but gain legal immunity and access proper access to data making it easier to train models without the threat of litigation over copyright infringement which can be both costly and time-consuming. India\u2019s hybrid model\u2014token fees for startups, revenue-sharing for giants\u2014and automated compliance via blockchain avoid\u00a0GDPR\u2019s pitfalls\u00a0<\/a>like administrative cost concerns that plagued its initial rollout, leveraging digital infrastructure and offering India data sovereignty and countering opposition through fairness and predictability.<\/p>\n Israel\u2019s tiered licensing system<\/a>\u00a0adopted in 2023 is proof that hybrid models (e.g., token fees for SMEs and revenue sharing for larger firms) make this work.\u00a0While broadcasting and AI differ in content, Section 31D(3) of the Indian Copyright Act supports variable pricing by setting different royalties for radio and TV, a principle upheld by courts to reflect paying capacity,\u00a0<\/em>This logic applies to AI firms as well, tailoring obligations to firm size, not industry, mirroring MSME exemptions in place (e.g., \u20b9 5 crores threshold), patent filings rose by 355% over 2016 to 2024 (refer<\/a>); this is an indication of how scalable frameworks nurture innovation (refer for data<\/a>), while broadcasting and AI training differ, the principle of tiered licensing-proven in Israel\u2019s 2023 AI policy and India\u2019s Section 31D(3)-ensures scalable, proportional fees for startups and revenue-sharing for larger firms. This approach addresses financial disparities by tailoring obligations to firm size, not content type. Thus, the regulatory logic, not the industry, justifies applying hybrid models to balance innovation and creator rights.<\/p>\n Expanding Section 31D to include machine learning (ML) training would subject AI companies to statutory licensing. Aligning ML data use with broadcasting\u2019s compulsory licensing model supports creators through revenue-sharing rights and ensures public access. Amendments need to make clear that \u201cbroadcasting\u201d encompasses algorithmic consumption of works.<\/p>\n![\"A](\"https:\/\/i0.wp.com\/spicyip.com\/wp-content\/uploads\/2025\/06\/image-3.png?w=500&ssl=1)
Reversing the Opt-Out Burden: Why AI Firms Should Bear Licensing Obligations for Training Data<\/h3>\n
At the outset, it\u2019s crucial to recognize the distinction between broadcasting and AI training. While broadcasting directly disseminates entire copyrighted works to the public (e.g., playing songs on the radio), AI training extracts patterns or data without public communication during the process as explained\u00a0here<\/a>. Yet, AI still monetizes creative labor by internalizing stylistic elements or producing outputs that compete with original works, as highlighted in cases like\u00a0Andersen v. Stability AI<\/em>\u00a0\u00a0and in arguments\u00a0here<\/a>. Redefining \u201cbroadcasting\u201d under Section 31D to include algorithmic dissemination establishes a duty to license, ensuring compensation for creators regardless of the mode of use. Moreover, even if courts rule that AI training qualifies as \u201cfair dealing\u201d under Section 52 of the Indian Copyright Act, as debated in ongoing litigation like\u00a0ANI v. OpenAI<\/em><\/a>, the argument for statutory licensing persists. Fair dealing may excuse non-expressive, pattern-based use, but the commercial scale of AI training and its potential to impact creator markets justify proactive compensation and transparency to uphold equity as seen in arguments\u00a0here<\/a>\u00a0and also in a\u00a0report by The U.S. Copyright Office<\/a>\u00a0which states \u201cMaking commercial use of vast troves of copyrighted works to produce expressive content that competes with them\u2026 goes beyond established fair use boundaries<\/em>\u201d. By adapting Section 31D, India can address these unique challenges and set a precedent for ethical AI governance.<\/p>\nI. Why Robots.txt Fails Creators and its Vulnerabilities.<\/h3>\n
II. Case Study: Lessons from India\u2019s Broadcasting Licensing Model<\/h3>\n
III. The New Framework: Statutory \u201cDuty to License\u201d<\/h3>\n
Proactive Disclosure<\/strong><\/h3>\n
IV. Addressing Counterarguments<\/h3>\n
Q1: \u201cWon\u2019t this hinder innovation?\u201d<\/strong><\/h4>\n
Principles from Broadcasting Applicable to AI<\/strong><\/em><\/h5>\n
1. Algorithmic Use \u2260 Public Communication<\/strong>
2. Scalable Royalty Models<\/strong><\/h5>\n
3. Legal Immunity<\/strong><\/h5>\n
Q.2 Will unclaimed works be dealt with under this statutory framework?<\/strong><\/h4>\n
Q3: \u201cOpt-out systems are simpler!\u201d\u00a0<\/strong><\/h4>\n
Q4: \u201cGlobal AI Firms Will Stay Away from India!\u201d\u00a0<\/strong><\/h4>\n
Q5: \u201cSmall Startups Don\u2019t Have Money to Pay Royalties!\u201d\u00a0<\/strong><\/h4>\n
V. Suggestions for Implementation<\/h3>\n
Copyright Act Amendments<\/strong><\/h4>\n
Digital India Act Integration<\/strong><\/h4>\n