Stanford NLP resurfaces Jurafsky & Liang's 'Foundation Models and Fair Use' paper
stanfordnlp · x · 2026-11-06
Stanford NLP retweets the JMLR paper "Foundation Models and Fair Use" by Henderson, Li, Jurafsky, Hashimoto, Lemley and Percy Liang. The paper analyzes fair use for foundation model training data, including code: verbatim copying of large code portions is unlikely fair use, algorithms and other non-literal elements fall outside copyright, and sufficiently transformative use may qualify — a canonical legal reference for AI copyright debates.
More from Safety
- AI uncovers 10-year-old bug where any text over 256 chars works as a valid root password — andersonbcdefg · 2026-11-06
- Anthropic chief architect Sam McCandlish's first post hints at internal-acceleration report, possibly hitting 2x RSP threshold — inductionheads · 2026-11-06
- Claude Code bug leaks user email via User-Agent header, victim reports dozens of scam emails daily — niloofar_mire · 2026-11-06
- Gary Marcus blasts Anthropic cofounder's AI-safety defense of METR report redactions — GaryMarcus · 2026-11-06
- Anthropic cofounder defends METR report redactions; Gary Marcus unimpressed — GaryMarcus · 2026-11-06
- AI-powered login attacks hit 11-year-old podcast site: 30% of all attempts in one day — NathanpmYoung · 2026-11-06