**OPENAI RESPONDS TO COPYRIGHT LAWSUIT**
OpenAI, the company behind ChatGPT, has claimed that the New York Times “paid someone to hack” their products and produce evidence for the copyright lawsuit filed by the newspaper.
The claim comes from the company’s lawyers, who responded to the New York Times lawsuit. In the document, OpenAI’s lawyers argue that the evidence for the newspaper’s case required “tens of thousands of attempts to be generated” and was obtained by “targeting and exploiting a bug” that the company is fixing.
**NEW YORK TIMES LAWSUIT**
“The NYT had to provide the tool with portions of the same articles from which they wanted to obtain textual passages, practically all of which were already present on various public websites. Normal people don’t use OpenAI products in this way,” the lawyers state.
The New York Times filed a lawsuit against OpenAI and Microsoft seeking “billions of dollars in legal and actual damages” for using its articles to train chatbots in December 2023.
OpenAI’s tools generate “results that verbatim recite the Times’ content, faithfully summarize it, and mimic its expressive style, as demonstrated by dozens of examples,” the lawsuit reads.
**OpenAI’s stance on ChatGPT and Fair Use**
The company had previously stated, in written evidence provided to the UK House of Lords, that it would be “impossible” to train AI tools without content sourced from the internet.
OpenAI’s lawyers also added that ChatGPT is not a “substitute for a New York Times subscription.”
“In the real world, people don’t use ChatGPT or any other OpenAI product for this purpose.” They argue that the Times’ articles were a “minimal part of the data used to train the chatbots.”
According to them, the use of copyrighted material for training is protected under fair use, a US legal doctrine that allows some unauthorized uses of copyrighted works.
**EXPERTS’ VIEW**
Recently, some experts told Euronews Next that lawsuits filed against AI companies for using copyrighted content for training will likely be determined on a case-by-case basis, based on the resemblance to the original work.
