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Tòa án tối cao Delhi thông báo OpenAI trong kháng cáo bản quyền ANI

Tòa án Tối cao Delhi đã ban hành thông báo chính thức tới OpenAI liên quan đến đơn kháng cáo của Asian News International (ANI) thách thức việc tòa án cấp dưới từ chối bảo vệ bản quyền tạm thời đối với dữ liệu đào tạo AI.

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Source-provided image accompanying Delhi High Court notices OpenAI in ANI copyright appeal
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Mô hình ngôn ngữ lớn (LLM)
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The Delhi High Court issued a formal notice to OpenAI on Tuesday in response to an appeal filed by Asian News International (ANI). The appeal challenges a July single-judge order that denied ANI interim copyright protection against OpenAI's use of its news archive for training large language models. A Division Bench comprising Justices Avneesh Jhingan and Manmeet Pritam Singh Arora has scheduled the next hearing for December to evaluate whether LLM training constitutes fair dealing or unauthorized commercial exploitation under Indian copyright law.

The Delhi High Court issued a formal notice to OpenAI on Tuesday, responding to an appeal filed by Asian News International (ANI). This appeal challenges a July ruling by Justice Amit Bansal, which had rejected ANI's plea for an interim injunction against OpenAI storing, reproducing, or using its news archive for training large language models.

The initial single-judge order held that OpenAI's retention of news content did not prima facie constitute copyright infringement under the Copyright Act, 1957. The judge reasoned that machine learning involves transformative processing and that factual news lacks copyright protection, noting that an injunction would cause irreparable harm to OpenAI and negatively impact the public interest.

ANI's counsel, Sidhant Kumar, argued before the Division Bench that Indian copyright law is governed by strict statutory 'fair dealing' exceptions under Section 52, rather than the broader US 'fair use' concept. ANI contended that commercial enterprise-scale data scraping cannot be shielded under personal or research exceptions, pointing to OpenAI's commercial licensing deals with global publishers as evidence of a viable licensing market.

OpenAI previously questioned the suit's maintainability in India, arguing that its model training occurs on US-based servers and that its web crawlers respect publisher opt-outs via blocklists. Senior Advocate Kapil Sibal, representing the Broadband India Forum which intervened in the initial proceedings, emphasized the case's implications for digital infrastructure and technology adoption in India.

The Division Bench declined to grant interim directions without hearing OpenAI's defense, scheduling the next hearing for December. The upcoming review will determine whether LLM training constitutes fair dealing or unauthorized commercial exploitation under Indian copyright law, a decision that will likely set a crucial legal benchmark for the AI industry.

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This legal development marks a critical stage in India's first major copyright litigation against a major AI developer, potentially establishing a binding precedent for how models may collect and train on copyrighted publishing content. The case directly tests the applicability of India's statutory 'fair dealing' exceptions versus the broader US 'fair use' doctrine in the context of commercial-scale data scraping. A ruling against OpenAI could force significant changes in data acquisition practices for AI companies operating in or targeting the Indian market, while a ruling in favor of OpenAI would likely validate current training methodologies for the industry globally.

This case represents the first major copyright litigation in India specifically targeting a leading AI developer, making it a pivotal test for the legal boundaries of AI training data usage in the country.

The distinction between 'fair dealing' and 'fair use' is central to the dispute. If the court rules that commercial-scale scraping for LLM training does not qualify for fair dealing exceptions, it could compel AI companies to seek explicit licenses from content creators, fundamentally altering the cost structure and data acquisition strategies for models.

The outcome will have significant practical implications for news agencies and publishers in India, potentially providing a legal framework to protect their intellectual property from unauthorized use in AI systems, or conversely, validating the current practice of training on publicly available web content.

As India becomes a major market for AI adoption, establishing clear legal guidelines for copyright in the context of machine learning is essential for both content creators and technology companies to operate with legal certainty.

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Core takeaway: Fine-tuning teaches models how to speak (form, style, syntax); RAG teaches models what to say (verifiable facts). Never use fine-tuning alone for factual memory.
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Xem gì tiếp theo

Monitor the December hearing for the Division Bench's decision on interim directions and the substantive merits of the appeal. Watch for OpenAI's formal legal response regarding its data collection practices and the potential impact on existing commercial licensing deals with publishers. Observe whether other news agencies or content creators join the litigation or file similar claims based on the outcome of this benchmark case.

The December hearing is the immediate next step, where the Division Bench will hear OpenAI's response and potentially issue interim directions or a substantive ruling on the appeal.

Legal experts will be watching for how the court interprets Section 52 of the Copyright Act, 1957, specifically regarding the scope of 'fair dealing' in the context of automated, large-scale data processing for commercial AI products.

The reaction of other media organizations and tech companies in India to the court's procedural and substantive decisions will indicate the broader industry impact and potential for similar litigation.

Any changes in OpenAI's data collection practices or licensing agreements in the Indian market following the court's decision will be a key indicator of the case's practical consequences.

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