Federal courts could, and should, play a more effective role in curtailing unnecessary government secrecy, argues Meredith Fuchs, general counsel at the National Security Archive, in a splendid new law review article.
“All too often, courts easily accept the argument that the executive needs unquestioning adherence to its judgments and that the court is not competent to assess those judgments in the realm of national security.”
“Yet judges have stemmed executive overreaching in other contexts involving national security claims. Judges have discretionary tools — such as the Vaughn Index, in camera review, and special master — available to help them do the same in the secrecy context,” she wrote.
Her article provides an updated introduction to the secrecy system, a critique of secrecy policy, and a survey of recent judicial actions.
See “Judging Secrets: The Role Courts Should Play in Preventing Unnecessary Secrecy” by Meredith Fuchs, Administrative Law Review, Winter 2006.
The Federation of American Scientists supports H.R. 4420, the Cool Corridors Act of 2025, which would reauthorize the Healthy Streets program through 2030 and seeks to increase green and other shade infrastructure in high-heat areas.
The current lack of public trust in AI risks inhibiting innovation and adoption of AI systems, meaning new methods will not be discovered and new benefits won’t be felt. A failure to uphold high standards in the technology we deploy will also place our nation at a strategic disadvantage compared to our competitors.
Using the NIST as an example, the Radiation Physics Building (still without the funding to complete its renovation) is crucial to national security and the medical community. If it were to go down (or away), every medical device in the United States that uses radiation would be decertified within 6 months, creating a significant single point of failure that cannot be quickly mitigated.
The federal government can support more proactive, efficient, and cost-effective resiliency planning by certifying predictive models to validate and publicly indicate their quality.