DOE Declassifies Declassification of Downblending Move
Last year, the Department of Energy decided to declassify the fact it intended to make 25 metric tons of Highly Enriched Uranium available from “the national security inventory” for downblending into Low Enriched Uranium for use in the production of tritium.
However, the decision to declassify that information was classified Secret.
This year, the Department of Energy decided to declassify the declassification decision, and it was disclosed last week under the Freedom of Information Act.
While the contortions in classification policy are hard to understand, the underlying move to downblend more HEU for tritium production probably makes sense. Among other things, it “delays the urgency — but doesn’t eliminate the eventual need — to build a new domestic enrichment capacity,” said Alan J. Kuperman of the University of Texas at Austin.
There were 160 MT of US HEU downblended by the end of FY 2018, according to the FY 2019 DOE budget request (volume 1, at page 474), and a total of 162 MT was anticipated by the end of FY 2019, as noted recently by the International Panel on Fissile Materials.
“The overall amount of HEU available for down-blending and the rate at which it will be down-blended is dependent upon decisions regarding the U.S. nuclear weapons stockpile, the pace of warhead dismantlement and receipt of HEU from research reactors, as well as other considerations, such as decisions on processing of additional HEU through H-Canyon, disposition paths for weapons containing HEU, etc,” according to the DOE budget request.
These ideas aim to advance the detailed policy solutions needed to foster public trust and implement fairness in the adoption of AI across diverse domains, from healthcare and government benefits to rural access, education, and worker protections.
The evidence is clear: algorithmic pay-setting is established in app-based work, and payroll/timekeeping failures show how software can produce systemic wage harm at scale
While a few states have taken steps to implement decision-making mechanisms for certain AI systems, too many leaders are simply accepting narratives about AI’s purported public benefit at face value – jumping to the “how” of AI implementation before thoroughly vetting potential systems and deciding whether they are appropriate to use at all.
When properly structured — with specific numeric targets, secured financial obligations, independent monitoring, and meaningful enforcement — CBAs transform data center deals into durable community partnerships.