# 德里高等法院驳回印度新闻机构 ANI 对 OpenAI 的版权禁令请求

- 来源：The Decoder：AI News（RSS）
- 作者：Matthias Bastian
- 发布时间：2026-07-28 01:55
- AIHOT 分数：67
- AIHOT 链接：https://aihot.virxact.com/items/cms3k3nsl00fqroiyeutm0wcv
- 原文链接：https://the-decoder.com/delhi-high-court-hands-openai-a-win-by-rejecting-major-indian-news-agencys-copyright-injunction

## AI 摘要

德里高等法院驳回印度新闻机构 ANI 对 OpenAI 的初步禁令请求，认为 ANI 未能证明 ChatGPT 逐字复制其文章，且提交的文章发表于模型训练之后。法官认定 OpenAI 与 ANI 分属不同行业，未造成经济损失，并肯定大语言模型对教育、研究和可访问性的公共利益。法院初步将 AI 训练视为“私人或个人使用，包括研究”的例外，但要求训练数据必须来自合法来源。

## 正文

Key Points

The Delhi High Court has rejected Indian news agency ANI's request for a preliminary injunction against OpenAI over copyright infringement.

ANI couldn't prove that ChatGPT reproduced its articles verbatim. The articles it submitted as evidence were published after the models had already been trained.

The judge found no economic harm to ANI because OpenAI and the news agency operate in different sectors, and affirmed the public benefit of language models for education, research, and accessibility.

In an interim ruling, the Delhi High Court rejected a request by Indian news agency Asian News International (ANI) for a preliminary injunction against OpenAI.

ANI, one of India's largest news agencies, sued OpenAI over its use of copyrighted material for AI training and in ChatGPT's outputs. Judge Amit Bansal denied the requested relief on both claims.

The decision addresses memorization, Retrieval Augmented Generation (RAG), and the legal status of AI training. AI copyright law expert Andres Guadamuz calls the ruling an important early win for OpenAI.

ANI's evidence undermined its copyright claims

ANI submitted several ChatGPT outputs to the court that it claimed were substantial copies of its articles. The move backfired because OpenAI showed that the models used, GPT-4 and GPT-4o, were trained on data from April 2022 and April 2024. The articles ANI cited were mostly from August and September 2024, so they couldn't have been part of the training data.

The judge's preliminary view was that the similarities came from RAG, which lets a language model retrieve online information in real time, much like a search engine. ANI hadn't addressed RAG in its filing, so the court couldn't make a final ruling on the issue. The judge said RAG-based outputs could qualify as "communication to the public," a question the court will address in the main proceedings.

ANI's case got weaker from there. The agency had used adversarial prompts, explicitly telling the model to reproduce articles "exactly." Even so, ANI couldn't produce a single verbatim copy. The judge found that facts in news articles in general aren't copyrightable and that reproducing topics and headlines didn't amount to direct competition with ANI in this case.

The evidence also didn't support ANI's claim that OpenAI permanently stores training data in its models and can reproduce the agency's work verbatim on demand. But the court will revisit that question in the main proceedings.

Court tentatively treats AI training as fair use

ANI also failed to show that copying its work for AI training amounted to copyright infringement. Both parties agreed that OpenAI had used ANI content during training, but OpenAI argued that the material made up a tiny share of the overall dataset and that the model extracted only non-expressive elements such as grammar, syntax, and language patterns.

The judge looked at exceptions under Indian copyright law and relied on a clause covering "private or personal use, including research," reading "research" broadly enough to cover AI training.

For that exception to hold, the judge set conditions. Training copies must come from lawful sources, not shadow libraries or paywalled sites accessed without permission. OpenAI also never made the training copies public and processed them only internally. Guadamuz says this is the first time a court has explicitly found that AI training falls under a private use exception.

The court ran a three-part fairness test and sided with OpenAI on all three counts. OpenAI's use of ANI's works was limited to training, since no memorization or reproduction was proven. ANI also couldn't show economic harm because the two companies operate in different sectors. Even when users ask ChatGPT about ANI headlines, the model only returns topics and, at most, a few article titles.

The judge cited U.S. cases including Bartz v. Anthropic and Kadrey v. Meta, where language model outputs were deemed transformative. He also pointed to the earlier Google Books ruling.

The judge also found that trained language models improve access to information, support education, advance scientific research, help with software development, enable translation, and create tools for people with disabilities.

International AI copyright cases paint a mixed picture

The Delhi ruling joins a growing list of court decisions worldwide that have reached conflicting conclusions. In the U.S., a judge threw out the lawsuit filed by Raw Story and AlterNet against OpenAI because the plaintiffs couldn't show enough harm and the odds of exact copies were low. That court also held that facts aren't copyrightable.

The GitHub Copilot case failed too, with plaintiffs unable to present a single example of identical code. The Intercept, on the other hand, won a partial victory through a DMCA complaint over copyrighted material that had been stripped out before training.

In Ross Intelligence v. Thomson Reuters, a court denied fair use because the AI research tool directly competed with Thomson Reuters' legal database Westlaw, making the use non-transformative. The court stressed that this ruling applied only to this non-generative use case and couldn't be extended to large language models.

In the Anthropic case, a federal court in San Francisco called AI training with copyrighted works "spectacularly" transformative, a strong signal favoring fair use. But the court drew a "Napster comparison" because Anthropic had used pirated books from shadow libraries as training data. Fair use doesn't cover unlawfully obtained material. Anthropic later paid $1.5 billion to book authors for using those pirated copies.

The U.S. Copyright Office rejected the AI industry's argument that training on "vast troves of copyrighted works" broadly qualifies as fair use. The official who wrote the report was fired by the Trump administration shortly after it was published.

In Europe, two courts reached opposite conclusions almost simultaneously. The Munich Regional Court ruled in the GEMA case that song lyrics were reproducible in the model weights, making it a copyright-relevant reproduction. The High Court in London dismissed the Getty Images v. Stability AI lawsuit, ruling that an AI model isn't an "infringing copy." New research showing that language models can memorize copyrighted books could ramp up the memorization debate further.

Across all these cases, the same core questions remain unresolved. Do AI models permanently store training data? Can training qualify as fair use? Where's the line between lawfully and unlawfully obtained data? And do copies generated through adversarial prompts reflect normal use?

AI and media face a problem beyond copyright

Beyond copyright, another question looms for the media industry. Even if courts rule that AI training is lawful, AI-powered search products could still gut the news market. A recent Pew Research Center study shows that click-through rates to external websites drop to just 8 percent with Google's AI Overviews, compared to 15 percent without an AI summary. Users tend to stop searching right after getting the AI response. They don't check other sources.

For news agencies like ANI, this means AI systems that summarize news and make clicking the original source unnecessary could erode the industry's business model over time, even without direct copyright infringement.

The Munich I Regional Court also recently ruled that Google is directly liable for false claims in its AI summaries, since these count as independent content rather than search results. The limited liability that traditionally shielded search engine operators doesn't extend to AI-generated summaries.

That ruling could become relevant for ChatGPT's RAG-based responses too. When AI systems summarize news and make independent claims, their operators effectively become media providers, with all the liability that comes with it. That shift could also force courts to reconsider fair use. One key factor in the fairness test is whether the new product competes with the works it was trained on. If AI summaries replace the need to visit news sites, judges may have a harder time ruling that the use is non-competitive.

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