All Work Published on Foundation Models
Stanford HAI researchers create eight new AI benchmarks that could help developers reduce bias in AI models, potentially making them fairer and less likely to case harm.
Stanford HAI researchers create eight new AI benchmarks that could help developers reduce bias in AI models, potentially making them fairer and less likely to case harm.
A key promise of machine learning is the ability to assist users with personal tasks.
A key promise of machine learning is the ability to assist users with personal tasks.
Foundation Models and Copyright Questions
This brief warns that fair use may not fully shield U.S. foundation models trained on copyrighted data and calls for combined legal and technical safeguards to protect creators.
This brief warns that fair use may not fully shield U.S. foundation models trained on copyrighted data and calls for combined legal and technical safeguards to protect creators.
A study led by Stanford HAI Faculty Fellow Johannes Eichstaedt reveals that large language models adapt their behavior to appear more likable when they are being studied, mirroring human tendencies to present favorably.
A study led by Stanford HAI Faculty Fellow Johannes Eichstaedt reveals that large language models adapt their behavior to appear more likable when they are being studied, mirroring human tendencies to present favorably.
Stanford scholars respond to a federal RFC on AI accountability policy issued by the National Telecommunications and Information Administration (NTIA).
Stanford scholars respond to a federal RFC on AI accountability policy issued by the National Telecommunications and Information Administration (NTIA).
Holistic Evaluation of Large Language Models for Medical Applications
Medical and AI experts build a benchmark for evaluation of LLMs grounded in real-world healthcare needs.
Medical and AI experts build a benchmark for evaluation of LLMs grounded in real-world healthcare needs.
