Under the Invention Secrecy Act of 1951, the government may impose a secrecy order on patent applications submitted to the Patent Office whenever the disclosure of the inventions described in such applications “might be detrimental to the national security.”
At the end of Fiscal Year 2006, there were 4,942 secrecy orders in effect, a slight increase from the previous year’s total of 4,915, according to data provided to Secrecy News by the U.S. Patent and Trademark Office under the Freedom of Information Act (and very promptly, too).
During 2006 itself, 108 new invention secrecy orders were imposed, while 81 were rescinded. The precise character of the inventions that were subjected to new controls could not be ascertained, which is the whole point. However, it should be possible, if logistically challenging, to identify inventions that were formerly subject to a secrecy order but are no longer. We haven’t tried to do so lately. But they typically involve technologies that have specific military applications.
The large majority of invention secrecy orders are imposed on patent applications in which the government has a property interest, perhaps having funded the development of the invention. But each year, there are also so-called “John Doe” secrecy orders which prohibit the disclosure of inventions created by private inventors or businesses where the government has no property interest, thereby raising thorny First Amendment issues. In 2006, there were 29 new “John Doe” invention secrecy orders.
The latest statistics and other background on invention secrecy can be found here.
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.