All Work Published on Regulation, Policy, Governance
“It connects back to my fear that the people with the fewest resources will be most affected by the downsides of AI,” says HAI Policy Fellow Riana Pfefferkorn in response to a viral AI-generated deepfake video.
“It connects back to my fear that the people with the fewest resources will be most affected by the downsides of AI,” says HAI Policy Fellow Riana Pfefferkorn in response to a viral AI-generated deepfake video.
This brief sheds light on the “regulatory misalignment” problem by considering the technical and institutional feasibility of four commonly proposed AI regulatory regimes.
This brief sheds light on the “regulatory misalignment” problem by considering the technical and institutional feasibility of four commonly proposed AI regulatory regimes.
"The biggest difference between 23andMe and other breaches is that sequenced DNA is 'irreplaceable and immutable,' said Jennifer King," a Stanford HAI Policy Fellow.
"The biggest difference between 23andMe and other breaches is that sequenced DNA is 'irreplaceable and immutable,' said Jennifer King," a Stanford HAI Policy Fellow.
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 Stanford study reveals that leading AI companies are pulling user conversations for training, highlighting privacy risks and a need for clearer policies.
A Stanford study reveals that leading AI companies are pulling user conversations for training, highlighting privacy risks and a need for clearer policies.
The Privacy-Bias Trade-Off
This brief examines the “regulatory misalignment” of existing regulatory proposals for AI and urges policymakers to first consider feasibility, trade-offs, and unintended consequences before rushing into regulation.
This brief examines the “regulatory misalignment” of existing regulatory proposals for AI and urges policymakers to first consider feasibility, trade-offs, and unintended consequences before rushing into regulation.
