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Hallucinating Law: Legal Mistakes with Large Language Models are Pervasive
A new study finds disturbing and pervasive errors among three popular models on a wide range of legal tasks.
A new study finds disturbing and pervasive errors among three popular models on a wide range of legal tasks.
This brief highlights the benefits of open foundation models and calls for greater focus on their marginal risks.
This brief highlights the benefits of open foundation models and calls for greater focus on their marginal risks.
Responses to OMB's Request for Comment on Draft Policy Guidance on Agency Use of AI
Stanford scholars respond to a federal RFC on the Office of Management and Budget’s (OMB) draft policy guidance on advancing governance, innovation, and risk management for agency use of AI.
Stanford scholars respond to a federal RFC on the Office of Management and Budget’s (OMB) draft policy guidance on advancing governance, innovation, and risk management for agency use of AI.
New Stanford tracker analyzes the 150 requirements of the White House Executive Order on AI and offers new insights into government priorities.
New Stanford tracker analyzes the 150 requirements of the White House Executive Order on AI and offers new insights into government priorities.
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.
America is ready again to lead on AI—and it won’t just be American companies shaping the AI landscape if the White House has anything to say about it.
America is ready again to lead on AI—and it won’t just be American companies shaping the AI landscape if the White House has anything to say about it.
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.
Daniel E. Ho's Testimony Before the Senate Committee on Homeland Security and Governmental Affairs
In this testimony presented to the U.S. Senate Committee on Homeland Security and Governmental Affairs, Daniel E. Ho calls for Congress to seize AI’s potential to modernize federal programs and protect civil rights to maintain American leadership in AI innovation and responsible AI.
In this testimony presented to the U.S. Senate Committee on Homeland Security and Governmental Affairs, Daniel E. Ho calls for Congress to seize AI’s potential to modernize federal programs and protect civil rights to maintain American leadership in AI innovation and responsible AI.
A diversity of perspectives from Stanford leaders in medicine, science, engineering, humanities, and the social sciences on how generative AI might affect their fields and our world
A diversity of perspectives from Stanford leaders in medicine, science, engineering, humanities, and the social sciences on how generative AI might affect their fields and our world
Implementation Challenges to Three Pillars of America’s AI Strategy
This white paper assesses the implementation status of three U.S. executive and legal actions related to AI innovation and trustworthy AI, calling for improvements in reporting and tracking key requirements.
This white paper assesses the implementation status of three U.S. executive and legal actions related to AI innovation and trustworthy AI, calling for improvements in reporting and tracking key requirements.
Jacob Goldin and Daniel E. Ho: Modernizing Tax Administration: AI, Efficiency, and Equity
This HAI seminar with Stanford professors Jacob Goldin and Daniel E. Ho discusses a collaboration on using AI to reshape a core function of government: collecting revenue.
Jacob Goldin and Daniel E. Ho: Modernizing Tax Administration: AI, Efficiency, and Equity
This HAI seminar with Stanford professors Jacob Goldin and Daniel E. Ho discusses a collaboration on using AI to reshape a core function of government: collecting revenue.
How to Build Academic-Public Health Partnerships: The Stanford - Santa Clara County Experience with COVID-19 Response
How to Build Academic-Public Health Partnerships: The Stanford - Santa Clara County Experience with COVID-19 Response