The Nuclear Regulatory Commission reported in July that it is “retiring” classification guides on three topical areas as a result of the ongoing Fundamental Classification Guidance Review.
The cancelled classification guides pertain to “national security information concerning nuclear materials and facilities”; “assessing nuclear threat messages”; and “information dealing with the release and dispersion of radioactive material.” To the extent that information on these topics may still require classification, the NRC report said there are other authorities that the Commission can rely on, including joint classification guides with the Department of Energy that remain in effect.
But the retired guides will no longer be available for use in classifying NRC information.
A similar report from the Department of Defense noted that 82 DoD classification guides had been eliminated under the Fundamental Classification Guidance Review as of last July.
The potential efficacy of a broad-based review of agency classification guidance in reducing excessive secrecy was demonstrated in practice by the Department of Energy in the mid-1990s. (The Department would “classify less, and enjoy it more,” a spokesman told Science Magazine in 1993.) Building on that example, I presented an argument for a government-wide review of classification guidance in “Reducing Government Secrecy: Finding What Works,” Yale Law & Policy Review, vol. 27, no. 2, spring 2009.
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.