Critics challenge AI fair use defense over training data format

Legal scholars and critics are challenging the core fair use defense employed by artificial intelligence companies in ongoing copyright litigation. Opponents argue that transforming copyrighted works into training datasets does not constitute transformative use if the resulting models can reproduce protected expression. This counterargument directly contests the industry's position that format conversion alone creates a new, non-infringing purpose. Plaintiffs assert that functional equivalence to the original work negates fair use protections regardless of technical processing methods. The dispute centers on whether intermediate copying for machine learning qualifies as fair use when outputs compete with source material. Legal experts note that courts have yet to establish binding precedent specifically addressing large-scale model training. The outcome of these arguments will likely define intellectual property standards for the entire generative AI sector. Industry stakeholders await judicial clarification on the boundaries of algorithmic transformation.