Q: What is the HHS AI strategy 2025? A: The HHS AI strategy 2025 is a public plan released by the U.S. Department of Health and Human Services in late 2025 to expand AI across internal operations, research, and public health while managing risk. It is organized around five pillars and promises inventories, governance structures, testing, and reporting to make AI a practical layer of value across the department. Q: What are the five pillars of the HHS AI strategy 2025? A: The five pillars are governance and risk management; shared infrastructure and platforms; workforce capability and burden reduction; “gold standard” research; and modernized service delivery. The strategy links these pillars to practices such as documentation, independent review, and continuous monitoring. Q: How will HHS identify and review high-impact AI systems? A: The strategy promises a central AI governance board, inventories of AI use cases, and explicit criteria for naming high-impact systems followed by documented assessments, independent review, pre-deployment testing, and ongoing monitoring. It also commits to plain-language public summaries for high-impact systems and maps its approach to the NIST AI Risk Management Framework. Q: What governance risks should watchdogs watch for under this strategy? A: Watchdogs should look for box-checking where reviews happen quickly with few findings, hidden policy choices buried in technical settings, vendor contracts that block access to model documentation, and weak safeguards for sensitive health data. The article also warns that metrics emphasizing speed can incentivize rushing projects rather than ensuring safety. Q: How should metrics be used to assess AI projects under the HHS AI strategy 2025? A: Under HHS AI strategy 2025, useful metrics include the share of high-impact systems with full independent review, number of issues found and fixed before launch, time from incident detection to mitigation, and bias and performance results by key groups. The article cautions against relying on speed-focused measures like average time to approval or projects launched per quarter because they can reward volume over safety. Q: What practical steps can agencies and vendors take to reduce AI governance risks? A: The article recommends building clear escalation and rollback triggers and giving reviewers documented authority to block launches and record reasons, publishing plain-language summaries and evaluation dashboards, minimizing and testing use of sensitive data, and writing contracts that require model documentation, incident reporting, and audit rights. It also advises mapping controls to the NIST AI RMF and ISO/IEC 42001-style plan–do–check–act cycles and tracking safety-focused metrics. Q: Is HHS seeking ISO/IEC 42001 certification for its AI management system? A: No, the article notes HHS is not seeking certification but is adopting many ISO/IEC 42001-style management practices such as plan–do–check–act cycles, documentation, independent review, and continuous monitoring. The department is building internal habits similar to a management system without pursuing external certification. Q: How can the public and watchdogs judge whether the HHS AI strategy 2025 is working? A: They can ask for specific disclosures and outcome metrics the article lists, such as how many high-impact systems were reviewed and delayed or stopped, how many public summaries were posted and whether they explain key design choices, what percent of vendor tools provide documentation and evaluation data, which privacy tests were run and their results, and how often post-deployment monitors triggered rollbacks or human review. Those questions help determine whether governance checks are real, data safeguards are strong, and policy choices are visible to the public.