All Work Published on Machine Learning
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.
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.
Scholars develop a new framework that optimizes compound AI systems by backpropagating large language model feedback.
Scholars develop a new framework that optimizes compound AI systems by backpropagating large language model feedback.
In risk modeling, AI researchers take a more-is-better approach to training data, but a new study argues that a less-is-more approach may be preferable.
In risk modeling, AI researchers take a more-is-better approach to training data, but a new study argues that a less-is-more approach may be preferable.