Stanford
University
  • Stanford Home
  • Maps & Directions
  • Search Stanford
  • Emergency Info
  • Terms of Use
  • Privacy
  • Copyright
  • Trademarks
  • Non-Discrimination
  • Accessibility
© Stanford University.  Stanford, California 94305.
Response to NTIA’s Request for Comment on Dual Use Open Foundation Models | Stanford HAI

Stay Up To Date

Get the latest news, advances in research, policy work, and education program updates from HAI in your inbox weekly.

Sign Up For Latest News

Navigate
  • About
  • Events
  • Careers
  • Search
Participate
  • Get Involved
  • Support HAI
  • Contact Us
Skip to content
  • About

    • About
    • People
    • Get Involved with HAI
    • Support HAI
  • Research

    • Research
    • Fellowship Programs
    • Grants
    • Student Affinity Groups
    • Centers & Labs
    • Research Publications
    • Research Partners
  • Education

    • Education
    • Executive and Professional Education
    • Government and Policymakers
    • K-12
    • Stanford Students
  • Policy

    • Policy
    • Policy Publications
    • Policymaker Education
    • Student Opportunities
  • AI Index

    • AI Index
    • AI Index Report
    • Global Vibrancy Tool
    • People
  • News
  • Events
  • Industry
  • Centers & Labs
policyResponse to Request

Response to NTIA’s Request for Comment on Dual Use Open Foundation Models

Date
March 27, 2024
Topics
Foundation Models
Regulation, Policy, Governance
Privacy, Safety, Security
Read Paper
abstract

In this response to the National Telecommunications and Information Administration’s NTIA) request for comment on dual use foundation AI models with widely available model weights, scholars from Stanford HAI, the Center for Research on Foundation Models (CRFM), the Regulation, Evaluation, and Governance Lab (RegLab), and other institutions urge policymakers to amplify the benefits of open foundation models while further assessing the extent of their marginal risks.

Read Paper
Share
Link copied to clipboard!
Authors
  • Researchers from Stanford HAI
  • CRFM null
  • RegLab null
  • Other Institutions

Related Publications

Policy Implications of DeepSeek AI’s Talent Base
Amy Zegart, Emerson Johnston
Quick ReadMay 06, 2025
Policy Brief

This brief presents an analysis of Chinese AI startup DeepSeek’s talent base and calls for U.S. policymakers to reinvest in competing to attract and retain global AI talent.

Policy Brief

Policy Implications of DeepSeek AI’s Talent Base

Amy Zegart, Emerson Johnston
International Affairs, International Security, International DevelopmentFoundation ModelsWorkforce, LaborQuick ReadMay 06

This brief presents an analysis of Chinese AI startup DeepSeek’s talent base and calls for U.S. policymakers to reinvest in competing to attract and retain global AI talent.

Response to OSTP’s Request for Information on the Development of an AI Action Plan
Caroline Meinhardt, Daniel Zhang, Rishi Bommasani, Jennifer King, Russell Wald, Percy Liang, Daniel E. Ho
Mar 17, 2025
Response to Request

In this response to a request for information issued by the National Science Foundation’s Networking and Information Technology Research and Development National Coordination Office (on behalf of the Office of Science and Technology Policy), scholars from Stanford HAI, CRFM, and RegLab urge policymakers to prioritize four areas of policy action in their AI Action Plan: 1) Promote open innovation as a strategic advantage for U.S. competitiveness; 2) Maintain U.S. AI leadership by promoting scientific innovation; 3) Craft evidence-based AI policy that protects Americans without stifling innovation; 4) Empower government leaders with resources and technical expertise to ensure a “whole-of-government” approach to AI governance.

Response to Request

Response to OSTP’s Request for Information on the Development of an AI Action Plan

Caroline Meinhardt, Daniel Zhang, Rishi Bommasani, Jennifer King, Russell Wald, Percy Liang, Daniel E. Ho
Regulation, Policy, GovernanceMar 17

In this response to a request for information issued by the National Science Foundation’s Networking and Information Technology Research and Development National Coordination Office (on behalf of the Office of Science and Technology Policy), scholars from Stanford HAI, CRFM, and RegLab urge policymakers to prioritize four areas of policy action in their AI Action Plan: 1) Promote open innovation as a strategic advantage for U.S. competitiveness; 2) Maintain U.S. AI leadership by promoting scientific innovation; 3) Craft evidence-based AI policy that protects Americans without stifling innovation; 4) Empower government leaders with resources and technical expertise to ensure a “whole-of-government” approach to AI governance.

Safeguarding Third-Party AI Research
Kevin Klyman, Shayne Longpre, Sayash Kapoor, Rishi Bommasani, Percy Liang, Peter Henderson
Feb 13, 2025
Policy Brief
Safeguarding third-party AI research

This brief examines the barriers to independent AI evaluation and proposes safe harbors to protect good-faith third-party research.

Policy Brief
Safeguarding third-party AI research

Safeguarding Third-Party AI Research

Kevin Klyman, Shayne Longpre, Sayash Kapoor, Rishi Bommasani, Percy Liang, Peter Henderson
Privacy, Safety, SecurityRegulation, Policy, GovernanceFeb 13

This brief examines the barriers to independent AI evaluation and proposes safe harbors to protect good-faith third-party research.

Assessing the Implementation of Federal AI Leadership and Compliance Mandates
Jennifer Wang, Mirac Suzgun, Caroline Meinhardt, Daniel Zhang, Kazia Nowacki, Daniel E. Ho
Jan 17, 2025
White Paper

This white paper assesses federal efforts to advance leadership on AI innovation and governance through recent executive actions and emphasizes the need for senior-level leadership to achieve a whole-of-government approach.

White Paper

Assessing the Implementation of Federal AI Leadership and Compliance Mandates

Jennifer Wang, Mirac Suzgun, Caroline Meinhardt, Daniel Zhang, Kazia Nowacki, Daniel E. Ho
Government, Public AdministrationRegulation, Policy, GovernanceJan 17

This white paper assesses federal efforts to advance leadership on AI innovation and governance through recent executive actions and emphasizes the need for senior-level leadership to achieve a whole-of-government approach.

OSZAR »