{"id":3924,"date":"2026-08-26T10:48:20","date_gmt":"2026-08-26T10:48:20","guid":{"rendered":"https:\/\/kingmakersiasacademy.com\/current-affairs\/?p=3924"},"modified":"2026-08-28T10:48:55","modified_gmt":"2026-08-28T10:48:55","slug":"copy-rights","status":"publish","type":"post","link":"https:\/\/kingmakersiasacademy.com\/current-affairs\/copy-rights\/","title":{"rendered":"Copy rights"},"content":{"rendered":"<h2><\/h2>\n<table>\n<tbody>\n<tr>\n<td>\n<blockquote><p><b>SYLLABUS: Indian Polity<\/b><\/p>\n<p><b>General Studies \u2013 : II <\/b><span style=\"font-weight: 400;\">Important aspects of governance, transparency and accountability, e-governance- applications, models, successes, limitations, and potential; citizens charters, transparency &amp; accountability and institutional and other measures.\u00a0\u00a0<\/span><\/p><\/blockquote>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><b>IN NEWS:<\/b><span style=\"font-weight: 400;\"> How AI firms are destroying physical books to train their models<\/span><\/p>\n<p><b>QUESTION: <\/b><span style=\"font-weight: 400;\">With the proliferation of generative AI, should copyright law continue to distinguish sharply between \u2018access to a work\u2019 and \u2018use of a work for machine learning\u2019? Examine the emerging legal and policy challenges.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">(15 marks, 250 words)<\/span><\/p>\n<p><b>ANSWER: <\/b><span style=\"font-weight: 400;\">The legal tension between <\/span><b>access to copyrighted works<\/b><span style=\"font-weight: 400;\"> (how AI models obtain training data) and <\/span><b>use of works for machine learning<\/b><span style=\"font-weight: 400;\"> (how models extract patterns, weight parameters, and generate outputs) has become the central battleground of 21st-century intellectual property law.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Traditionally, copyright law treats &#8220;access&#8221; (lawful possession or viewing) and &#8220;use&#8221; (reproduction, adaptation, performance) as distinct concepts. However, generative AI blurs this boundary because machine learning requires the wholesale copying and ingestion of digital works just to &#8220;read&#8221; or &#8220;learn&#8221; from them.<\/span><\/p>\n<h3><b>The Traditional Paradigm: Access vs. Use<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">In traditional copyright frameworks, the distinction served clear legal functions:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Access:<\/b><span style=\"font-weight: 400;\"> Refers to obtaining entry to a work (e.g., buying a book, streaming a song, or bypassing a paywall). Access alone does not trigger copyright infringement unless accompanied by an unauthorized reproduction or public performance.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Use:<\/b><span style=\"font-weight: 400;\"> Refers to actions governed by exclusive rights under copyright (e.g., copying, modifying, distributing). Reading a book to learn writing techniques is a non-infringing &#8220;use,&#8221; while scanning and printing thousands of copies is infringing.<\/span><\/li>\n<\/ul>\n<h3><b>Why Machine Learning Collapses the Distinction<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Generative AI disrupts this dichotomy because of the technical reality of training foundational models:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Ingestion Requires Intermediate Copying:<\/b><span style=\"font-weight: 400;\"> Unlike a human who can read a book visually without reproducing it, a computer must create digital copies (scraping, downloading, parsing) to convert raw text or images into mathematical representations (vectors and embeddings).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Access Dictates Capacity:<\/b><span style=\"font-weight: 400;\"> The line between lawful access and unauthorized use becomes razor-thin when models scrape publicly accessible data that is nevertheless protected by copyright or subject to Terms of Service (ToS) anti-scraping provisions.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Extraction of Unprotectable Ideas via Expressive Copying:<\/b><span style=\"font-weight: 400;\"> Copyright protects <\/span><i><span style=\"font-weight: 400;\">expression<\/span><\/i><span style=\"font-weight: 400;\">, not <\/span><i><span style=\"font-weight: 400;\">ideas, facts, or styles<\/span><\/i><span style=\"font-weight: 400;\">. ML training uses copyrighted expressions strictly to extract unprotectable statistical relationships. Rights holders argue the copy itself is an unauthorized &#8220;use,&#8221; while AI developers argue it is merely an operational step toward non-infringing pattern learning.<\/span><\/li>\n<\/ol>\n<h3><b>Key Legal &amp; Policy Challenges<\/b><\/h3>\n<h4><b>1. The Elasticity of Fair Use and TDM Exceptions<\/b><\/h4>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>United States (Fair Use &#8211; Section 107):<\/b><span style=\"font-weight: 400;\"> Litigants debate whether ML ingestion qualifies as &#8220;transformative use&#8221; (similar to search engine indexing in <\/span><i><span style=\"font-weight: 400;\">Authors Guild v. Google<\/span><\/i><span style=\"font-weight: 400;\">) or constitutes market substitution. While the intermediate copying serves a transformative function, the output can directly compete with the creators whose work was ingested.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>European Union &amp; Other Jurisdictions:<\/b><span style=\"font-weight: 400;\"> The EU Digital Single Market Directive (Articles 3 &amp; 4) provides explicit Text and Data Mining (TDM) exceptions, distinguishing between commercial and research uses while allowing rights holders to &#8220;opt-out.&#8221; This explicitly decouples basic access from commercial ML extraction rights.<\/span><\/li>\n<\/ul>\n<h4><b>2. Mass Market Licensing vs. Market Failure<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">If every instance of ML training requires an explicit license, transaction costs could lock out open-source developers and entrench tech monopolies capable of paying blanket royalties. Conversely, failing to require licenses risks delegitimizing creators&#8217; economic interests, leading to systemic market failure for human artists.<\/span><\/p>\n<h4><b>3. Technical Measures and Paywalls (&#8220;Access Controls&#8221;)<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">When AI companies use web crawlers to bypass anti-scraping tools, robots.txt files, or paywalls, claims shift from standard copyright infringement to violations of the Digital Millennium Copyright Act (DMCA Section 1201 &#8211; Circumvention of Technological Measures) and breach of contract (ToS violations). This shifts the dispute entirely back to the &#8220;access&#8221; domain.<\/span><\/p>\n<h4><b>4. The Output Problem: Memorization and Substantial Similarity<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">While training involves ingestion (access\/use), outputs occasionally reproduce copyrighted snippets verbatim (&#8220;memorization&#8221;). This exposes AI developers to direct infringement claims, proving that access and output use remain intertwined.<\/span><\/p>\n<h3><b>Policy Pathways: Should the Law Maintain the Sharp Distinction?<\/b><\/h3>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Maintain the Sharp Distinction (Status Quo \/ Fair Use Focus):<\/b><span style=\"font-weight: 400;\"> This approach treats intermediate ingestion as non-infringing fair use, focusing copyright scrutiny strictly on market-competing outputs. While it strongly encourages technological innovation, it risks depleting the economic incentives for human creators.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Collapse the Distinction (Strict Permission Model):<\/b><span style=\"font-weight: 400;\"> Under this model, explicit authorization is required for both initial access and subsequent ML training uses. This protects creator rights but creates high transaction costs and risks monopolizing AI development among well-funded tech giants.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Hybrid Statutory Frameworks (Compulsory Licensing &amp; Opt-Outs):<\/b><span style=\"font-weight: 400;\"> This framework allows broad access for training by default, coupled with statutory remuneration schemes or machine-readable opt-outs (similar to the EU model). It balances AI scaling with creator compensation, though it requires complex international enforcement mechanisms.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Copyright law <\/span><b>should not completely abandon<\/b><span style=\"font-weight: 400;\"> the distinction between access and use, as doing so risks establishing a monopoly over reading, observing, and extracting factual knowledge. However, relying on a rigid, pre-digital binary is no longer workable.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Policy must evolve toward <\/span><b>remunerative middle-ground regimes<\/b><span style=\"font-weight: 400;\">\u2014such as standardized statutory licensing, robust opt-out standards, and transparency mandates\u2014that permit functional access for data processing while ensuring creators share in the economic value generated by machine learning.<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td>\n<blockquote><p><b>PYQ REFERENCE UPSC 2020<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Q. &#8220;What are the key features of the National Intellectual Property Rights Policy, 2016? How far has it succeeded in fostering creativity and innovation in India?&#8221;<\/span><\/p><\/blockquote>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><b>SOURCE: <\/b><a href=\"https:\/\/epaper.thehindu.com\/ccidist-ws\/th\/th_international\/issues\/201201\/OPS\/GQMGDL8FR.1+GJLGEQ4M1.1.html\" target=\"_blank\" rel=\"noopener\">The Hindu e-paper &#8211; How AI firms are destroying physical books to train their models<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>SYLLABUS: Indian Polity General Studies \u2013 : II Important aspects of governance, transparency and accountability, e-governance- applications, models, successes, limitations, 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