Working public endeavor
Measurable, responsible AI adoption in real work
Develop, validate, and disseminate privacy-preserving methods that help U.S. organizations, educators, workforce programs, professionals, and technical teams test generative-AI workflows, measure real outcomes, and implement appropriate human oversight.
The problem
AI teams can produce demonstrations quickly, but often lack comparable baselines, fixed evaluation cases, follow-up measurements, privacy-safe reporting, and an auditable record of human decisions.
Intended public benefit
Reusable evaluation records can help practitioners distinguish a promising demo from an accountable workflow and make adoption decisions using measured quality, cost, risk, and human-oversight evidence.
Current evidence state
Claims begin at zero and move only with proof.
- 3
- author-controlled working methods
- 0
- independently verified adoptions
- 0
- reviewed outcomes
- 0
- verified external citations
Milestones and status
| Window | Target | Status |
|---|---|---|
| 0–90 days | Release three working specifications, their schemas, examples, tests, and public correction process. | in-progress |
| 3–6 months | Complete governed pilots with independently verifiable U.S. participants and publish both positive and negative results. | not-started |
| 6–12 months | Publish reviewed replications and the first privacy-safe benchmark only after cohort gates pass. | not-started |
| 12–24 months | Document external derivative use, citations, reviewer feedback, corrections, and sustained method development. | not-started |
What this page does not claim
- This working statement is not evidence of national impact by itself.
- AI Amigos currently publishes no independently verified adoption or outcome claim.
- The four practice tracks are application settings for one evidence method, not four unrelated endeavors.
Public-policy context
The operating method is informed by public guidance on governance, mapping, measurement, management, performance, and monitoring. These sources support the problem context; they do not endorse AI Amigos.
- NIST AI Risk Management Framework - National Institute of Standards and Technology
- Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile - National Institute of Standards and Technology
- Artificial Intelligence: An Accountability Framework for Federal Agencies and Other Entities - U.S. Government Accountability Office
Change control
Owner: Vijay Bhoyar. Status: working-endeavor. Material changes are dated in the public timeline and never backdated.