All Work Published on Machine Learning
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.
Despite huge advancements in machine learning and neural networks, AI systems still depend on human direction. This article references HAI's 2022 conference where attendees were encouraged to rethink AI systems with a “human in the loop” and consider a future where people remain at the center of decision making.
Despite huge advancements in machine learning and neural networks, AI systems still depend on human direction. This article references HAI's 2022 conference where attendees were encouraged to rethink AI systems with a “human in the loop” and consider a future where people remain at the center of decision making.
Fareed Zakaria speaks with “Godmother of AI” Fei-Fei Li about her journey as a computer scientist and how it influenced the discovery of modern AI.
Fareed Zakaria speaks with “Godmother of AI” Fei-Fei Li about her journey as a computer scientist and how it influenced the discovery of modern AI.
Six interdisciplinary research teams received a total of $3 million to pursue groundbreaking ideas in the field of AI.
Six interdisciplinary research teams received a total of $3 million to pursue groundbreaking ideas in the field of AI.
