All Work Published on Foundation Models
At a recent Stanford-MIT-Princeton workshop, experts highlight the need for legal protections, standardized evaluation practices, and better terminology to support third-party AI evaluations.
At a recent Stanford-MIT-Princeton workshop, experts highlight the need for legal protections, standardized evaluation practices, and better terminology to support third-party AI evaluations.
Rishi Bommasani, Society Lead at HAI's CRFM, discusses where AI is proving most dangerous, why openness is important, and how regulators are thinking about the open-close divide.
Rishi Bommasani, Society Lead at HAI's CRFM, discusses where AI is proving most dangerous, why openness is important, and how regulators are thinking about the open-close divide.
Stanford's RegLab, directed by HAI Senior Fellow Daniel E. Ho, developed an AI model that helped Santa Clara accelerate the process of flagging and mapping restrictive covenants.
Stanford's RegLab, directed by HAI Senior Fellow Daniel E. Ho, developed an AI model that helped Santa Clara accelerate the process of flagging and mapping restrictive covenants.
Stanford RegLab, Princeton, and the County of Santa Clara Collaborate to Use AI to Identify and Map Racial Covenants from over 5 Million Deed Records
The approach paves the way for faster and more accurate compliance with California’s anti-discrimination law.
The approach paves the way for faster and more accurate compliance with California’s anti-discrimination law.
Dan Ho, HAI Senior Fellow and director of the Stanford RegLab, discusses RegLab's AI model that analyzes decades of property records, helping to identify illegal racially restrictive language in housing documents.
Dan Ho, HAI Senior Fellow and director of the Stanford RegLab, discusses RegLab's AI model that analyzes decades of property records, helping to identify illegal racially restrictive language in housing documents.
AI expert Gary Marcus references HAI's study showing that LLM responses to medical questions highly vary and are often inaccurate.
AI expert Gary Marcus references HAI's study showing that LLM responses to medical questions highly vary and are often inaccurate.