Mentor’s Comment

The editorial discusses India’s copyright challenge in the age of Artificial Intelligence (AI). It explains that old copyright laws, designed for the print era, are conflicting with AI, data mining, and digital access to knowledge. The article examines historical trends, policy gaps, global comparisons, and governance choices to understand how India can balance creator rights with innovation, accessibility, and public interest.

Why in the News?

The rise of Artificial Intelligence has triggered a major debate on copyright laws because AI systems require large-scale data for training, while existing copyright rules remain restrictive and outdated. India is now at a policy crossroads where balancing creator rights with innovation and public access has become urgent, making the issue highly relevant for governance and digital regulation today.

Introduction

Copyright law historically aimed to reward creators while ensuring eventual public access to knowledge. Over time, however, protections expanded in duration and scope, producing a system described as “copyright maximalism.” The emergence of AI disrupts this balance because machine learning requires extensive data scraping and analysis, challenging traditional definitions of copying, authorship, and creative ownership.

How has copyright law evolved from incentive to over-expansion?

1. Historical Purpose: Ensures limited monopoly rights for creators to incentivize publishing and dissemination of knowledge; early laws allowed controlled duplication mainly for printing and libraries.
2. Expansion of Duration: Extends protection for the author’s lifetime plus long posthumous periods, reducing entry into the public domain and restricting reuse.
3. Shift Toward Maximalism: Expands copyright beyond publication to automatic ownership of every created work, including minor or functional outputs.
4. Outcome: Restricts free circulation of knowledge despite original intent of encouraging creativity.

Why does AI challenge traditional copyright assumptions?

1. Data Dependency: Requires large volumes of digital text, images, and media for training; machine learning models process patterns rather than reproduce works directly.
2. Functional Use vs Creative Use: Treats data as statistical input rather than expressive content, questioning whether it constitutes infringement.
3. Scale Problem: AI systems must crawl massive portions of the internet, making individual licensing impractical.
4. Governance Gap: Creates uncertainty for developers, researchers, and innovation ecosystems in absence of clear legal exemptions.

What does global evidence reveal about text and data mining policies?

1. Comparative Study Findings: Survey across multiple countries showed most jurisdictions lack clear exceptions permitting large-scale AI data mining.
2. Legal Restriction: Countries such as the Philippines and Sri Lanka largely prohibit unrestricted copying for analysis.
3. Progressive Models: Japan and Singapore allow broader text and data mining exceptions, enabling technological experimentation.
4. Policy Implication: Flexible copyright frameworks correlate with stronger AI innovation environments.

Why is India’s current approach seen as a regulatory risk?

1. Absence of Explicit Exception: Indian copyright law does not clearly permit AI-oriented data mining.
2. Innovation Uncertainty: Creates legal ambiguity for startups, research labs, and academic institutions.
3. Comparative Disadvantage: Countries providing explicit exemptions attract advanced AI research and investment.
4. Governance Concern: Raises institutional accountability questions on balancing creator rights and technological advancement.

How does copyright intersect with accessibility and public interest?

1. Access to Knowledge: Restrictive copyright historically limited access for visually impaired persons until exceptions were introduced via the Marrakesh framework.
2. Policy Lesson: Demonstrates that flexible copyright exceptions can advance inclusion without undermining creativity.
3. Public Interest Principle: Supports constitutional goals of equality and knowledge dissemination.
4. Institutional Responsibility: Requires regulators to ensure copyright does not hinder socially beneficial technological use.

Should copyright protect jobs or creativity in the AI era?

1. Technological Transition: Historical innovations eliminated certain roles but created new industries and employment forms.
2. Policy Perspective: Copyright aims to encourage creativity, not permanently preserve existing occupations.
3. Institutional Role: Governments may support displaced workers through taxation and social policy instead of restricting innovation.
4. Outcome: Supports balanced governance between innovation and welfare measures.

What should copyright law protect in an AI-driven future?

1. Human Contribution: Protects genuine creative expression rather than data patterns or computational processes.
2. Openness Principle: Encourages collaborative innovation and derivative creativity.
3. Regulatory Reform: Requires modern exceptions for AI training, research use, and data mining.
4. Strategic Objective: Aligns copyright with digital economy goals while maintaining creator incentives.

Conclusion

India’s copyright framework stands at a policy crossroads where rigid protection models confront AI-led innovation. A balanced approach that safeguards genuine creativity while enabling data-driven technological development is necessary. Clear legal exceptions, institutional foresight, and alignment with constitutional values of access and innovation can help restore equilibrium between creators, technology, and public interest.

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