Bill Overview
Title: AI Training Act
Description: This bill requires the Office of Management and Budget (OMB) to establish or otherwise provide an artificial intelligence (AI) training program for the acquisition workforce of executive agencies (e.g., those responsible for program management or logistics), with exceptions. The purpose of the program is to ensure that the workforce has knowledge of the capabilities and risks associated with AI. The OMB must (1) update the program at least every two years, and (2) ensure there is a way to understand and measure the participation of the workforce and to receive and consider feedback from program participants.
Sponsors: Rep. Maloney, Carolyn B. [D-NY-12]
Target Audience
Population: Individuals in the acquisition workforce of US executive agencies
Estimated Size: 450000
- The executive agencies referred to likely employ thousands to hundreds of thousands of workers across various departments.
- Program management and logistics are integral components of many governmental functions and involve a significant number of personnel.
- AI training would directly impact those engaged in the development, management, and procurement within these agencies.
- The need to update the program every two years suggests ongoing training needs, increasing the target base over time.
- AI is becoming integral across various governmental and private sectors, enhancing the necessity for widespread training and adaptation.
Reasoning
- The policy targets a specific group: the acquisition workforce within U.S. federal executive agencies, estimated at about 450,000 people.
- The budget is substantial, suggesting a wide-reaching program with potential high-impact training tailored for specific needs of those in program management and logistics.
- It's important to include individuals who are peripherally affected, such as those interacting with trained staff or impacted by federal contracting changes due to improved AI understanding.
- The duration and impact of the policy must account for the two-year update cycle, affecting both short-term and long-term training benefits.
- While the primary impact is on the acquisition workforce, improvements in AI understanding could indirectly benefit stakeholders interacting with these government functions, like businesses and contractors.
Simulated Interviews
Logistics Manager at a federal agency (Washington, D.C.)
Age: 35 | Gender: female
Wellbeing Before Policy: 7
Duration of Impact: 20.0 years
Commonness: 15/20
Statement of Opinion:
- The training will help streamline our processes and make our supply chains more efficient.
- I'm worried about the time commitment required for the training.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 8 | 7 |
| Year 2 | 8 | 7 |
| Year 3 | 8 | 6 |
| Year 5 | 9 | 6 |
| Year 10 | 9 | 6 |
| Year 20 | 9 | 5 |
IT Specialist at a federal contracting office (Denver, Colorado)
Age: 42 | Gender: male
Wellbeing Before Policy: 6
Duration of Impact: 10.0 years
Commonness: 12/20
Statement of Opinion:
- Excited about learning new AI skills that could enhance our current IT solutions.
- Concerned about how relevant the material will actually be to my role.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 6 |
| Year 2 | 7 | 6 |
| Year 3 | 8 | 6 |
| Year 5 | 8 | 6 |
| Year 10 | 8 | 5 |
| Year 20 | 7 | 5 |
AI Consultant (San Francisco, California)
Age: 29 | Gender: other
Wellbeing Before Policy: 8
Duration of Impact: 5.0 years
Commonness: 5/20
Statement of Opinion:
- Optimistic that more trained personnel will make contracting with agencies smoother.
- My role is likely unchanged, but improved knowledge in agency contacts is beneficial.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 8 | 8 |
| Year 2 | 8 | 7 |
| Year 3 | 8 | 7 |
| Year 5 | 8 | 7 |
| Year 10 | 8 | 7 |
| Year 20 | 7 | 6 |
Program Manager at NASA (Houston, Texas)
Age: 50 | Gender: male
Wellbeing Before Policy: 9
Duration of Impact: 10.0 years
Commonness: 10/20
Statement of Opinion:
- The initiative is crucial for agencies keen on integrating AI.
- Already have significant AI experience, so personal impact may be limited.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 9 | 9 |
| Year 2 | 9 | 9 |
| Year 3 | 9 | 8 |
| Year 5 | 9 | 8 |
| Year 10 | 9 | 7 |
| Year 20 | 8 | 7 |
Training Coordinator at a federal agency (Atlanta, Georgia)
Age: 33 | Gender: female
Wellbeing Before Policy: 7
Duration of Impact: 15.0 years
Commonness: 8/20
Statement of Opinion:
- This is an exciting opportunity to expand our training capabilities and integrate AI concepts.
- Logistics of roll-out are challenging given current budgets.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 8 | 7 |
| Year 2 | 9 | 6 |
| Year 3 | 9 | 6 |
| Year 5 | 9 | 6 |
| Year 10 | 9 | 6 |
| Year 20 | 8 | 5 |
Entry-Level Procurement Officer (Miami, Florida)
Age: 27 | Gender: female
Wellbeing Before Policy: 6
Duration of Impact: 15.0 years
Commonness: 18/20
Statement of Opinion:
- Great educational advancement, will be beneficial for career growth in procurement.
- Worried about the technical jargon and pace of learning.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 6 |
| Year 2 | 8 | 6 |
| Year 3 | 8 | 5 |
| Year 5 | 8 | 5 |
| Year 10 | 8 | 5 |
| Year 20 | 7 | 5 |
Senior Advocate for AI in Government (Chicago, Illinois)
Age: 60 | Gender: male
Wellbeing Before Policy: 9
Duration of Impact: 10.0 years
Commonness: 9/20
Statement of Opinion:
- A much-needed push towards universal AI literacy in government.
- Should have been implemented sooner, but glad it’s happening now.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 9 | 9 |
| Year 2 | 9 | 8 |
| Year 3 | 9 | 8 |
| Year 5 | 9 | 7 |
| Year 10 | 8 | 7 |
| Year 20 | 8 | 7 |
Chief Procurement Officer (Seattle, Washington)
Age: 45 | Gender: male
Wellbeing Before Policy: 7
Duration of Impact: 20.0 years
Commonness: 13/20
Statement of Opinion:
- Excited about AI tools further revolutionizing procurement processes.
- Hope training will go beyond basics and cover advanced analysis.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 8 | 7 |
| Year 2 | 8 | 6 |
| Year 3 | 9 | 6 |
| Year 5 | 9 | 6 |
| Year 10 | 9 | 6 |
| Year 20 | 9 | 5 |
Logistics Coordinator (Phoenix, Arizona)
Age: 39 | Gender: female
Wellbeing Before Policy: 6
Duration of Impact: 20.0 years
Commonness: 14/20
Statement of Opinion:
- Finally, AI training tailored for logistics! This should boost efficiency.
- Worried that the rollout might encounter delays and budget issues.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 6 |
| Year 2 | 7 | 6 |
| Year 3 | 8 | 6 |
| Year 5 | 8 | 5 |
| Year 10 | 8 | 5 |
| Year 20 | 8 | 5 |
Risk Management Specialist (Boston, Massachusetts)
Age: 52 | Gender: male
Wellbeing Before Policy: 8
Duration of Impact: 10.0 years
Commonness: 7/20
Statement of Opinion:
- A crucial step for proper understanding of AI risks; should improve security and ethics.
- Concerned about keeping the curriculum updated with rapid AI advancements.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 9 | 8 |
| Year 2 | 9 | 8 |
| Year 3 | 9 | 8 |
| Year 5 | 9 | 7 |
| Year 10 | 9 | 7 |
| Year 20 | 8 | 6 |
Cost Estimates
Year 1: $90000000 (Low: $70000000, High: $110000000)
Year 2: $80000000 (Low: $60000000, High: $100000000)
Year 3: $85000000 (Low: $65000000, High: $105000000)
Year 5: $85000000 (Low: $65000000, High: $105000000)
Year 10: $85000000 (Low: $65000000, High: $105000000)
Year 100: $85000000 (Low: $65000000, High: $105000000)
Key Considerations
- Effective implementation and accessibility of AI training are crucial for widespread impact.
- Biannual program updates will require consistent budget allocation to remain relevant.
- The feedback mechanism should be robust enough to facilitate continuous program improvement.
- Monitoring the impact on productivity and cost efficiency across agencies will be essential to justify ongoing expenditures.