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 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. Ad DEC_D_Incontent-1

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.

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.

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：https://the-decoder.com/anthropic-won-a-fair-use-hearing-that-could-end-up-being-a-defeat/ and Kadrey v. Meta：https://the-decoder.com/metas-libgen-controversy-reveals-how-desperate-ai-companies-are-for-quality-training-data/, where language model outputs were deemed transformative. He also pointed to the earlier Google Books ruling：https://www.lto.de/recht/hintergruende/h/google-books-projekt-autoren-urheberrecht-fair-use.

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.

In Ross Intelligence v. Thomson Reuters：https://the-decoder.com/us-court-rejects-ai-startups-fair-use-defence-but-impact-on-openai-and-others-may-be-limited/, 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.

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?

The Munich I Regional Court also recently ruled that Google is directly liable for false claims in its AI summaries：https://the-decoder.com/landmark-german-ruling-declares-googles-ai-overviews-are-googles-own-words-and-makes-it-liable-for-false-answers/, 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.

Stay in the loop on AI. Clear, useful, no fluff.

Follow The Decoder for AI news, background stories and expert analyses.

The Decoder：https://the-decoder.com/