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Stanford HAI's implementation tracker
webCredibility Rating
4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: Stanford HAI
This tracker is particularly useful for following U.S. federal AI governance developments, including actions stemming from executive orders relevant to AI safety and compute governance.
Metadata
Importance: 55/100organizational reportreference
Summary
Stanford HAI's policy tracker monitors the implementation status of U.S. executive actions related to artificial intelligence, including executive orders and directives. It provides a structured overview of which AI-related federal mandates have been fulfilled, are in progress, or remain pending. This serves as a reference tool for researchers and policymakers tracking the regulatory landscape.
Key Points
- •Tracks compliance and implementation status of U.S. executive orders on AI across federal agencies
- •Provides a structured, up-to-date record of government AI policy actions and deadlines
- •Useful for monitoring how executive-level AI governance directives translate into actual policy
- •Relevant to understanding the U.S. approach to AI oversight, including compute thresholds and safety standards
- •Maintained by Stanford HAI, a leading academic institution in AI policy research
Cited by 2 pages
| Page | Type | Quality |
|---|---|---|
| Compute Monitoring | Approach | 69.0 |
| US Executive Order on Safe, Secure, and Trustworthy AI | Policy | 91.0 |
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Tracking U.S. Executive Action on AI | Stanford HAI Skip to content
Tracking U.S. Executive Action on AI
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White Paper (January 2025): Assessing the Implementation of Federal AI Leadership and Compliance Mandates
Relevant executive actions: EO 14110 (2023) and OMB Memo M-24-10 (2024)
In this white paper, scholars from Stanford HAI, the Stanford RegLab, and the Administrative Conference of the United States assess the implementation of a few key requirements included in two executive actions taken by the Biden administration: Executive Order 14110 on the Safe, Secure, and Trustworthy Development and Use of AI and the corresponding Office of Management and Budget Memorandum M-24-10 on Advancing Governance, Innovation, and Risk Management for Agency Use of AI (M-Memo), both of which have since been rescinded.
This review of agencies’ implementation of mandates to appoint Chief AI Officers and issue plans for complying with the M-Memo, as well as their budgetary allocations to support AI-related initiatives, shows that White House leadership and agencies have taken significant steps toward organizing and elevating AI leadership. The white paper also points to areas in need of improvement . A “whole-of-government” approach to AI innovation continues to require senior-level leadership that shepherds consistent compliance across distinct government agencies.
Read the Full White Paper
AI EO Tracker (2023-2024):
Analyzing the Safe, Secure, and Trustworthy AI EO
White Paper (December 2022): Implementation Challenges to Three Pillars of America’s AI Strategy
Relevant executive actions: AI In Government Act (2020), EO 13859 (2019), EO 13960 (2020)
This white paper, published in collaboration with the Stanford RegLab, assesses the progress of three pillars of U.S. leadership in AI innovation and trustworthy AI under the first Trump administration that carry the force of law: (i) the AI in Government Act of 2020; (ii) Executive Order 13859 on “AI Leadership”; and (iii) Executive Order 13960 on “AI in Government.” Collectively, these EOs and the AI in Government Act have been critical to defining the U.S. national strategy on AI and envisioning an ecosystem where the U.S. government leads in AI and promotes trustworthy AI.
We systematically examined the implementation status of each requirement and performed a comprehensive search across 200-plus federal agencies to assess implementation of key requirements to identify regulatory authorities pertaining to AI and to enumerate AI use cases. While much progress has been made, our findings are sobering. America’s AI innovation ecosystem is threatened by weak and inconsistent implementation of
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