There were 5,135 inventions that were under secrecy orders at the end of Fiscal Year 2010, the U.S. Patent and Trademark Office told Secrecy News last week. It’s a 1% rise over the year before, and the highest total in more than a decade.
Under the Invention Secrecy Act of 1951, patent applications on new inventions can be subject to secrecy orders restricting their publication if government agencies believe that disclosure would be “detrimental to the national security.”
The current list of technology areas that is used to screen patent applications for possible restriction under the Invention Secrecy Act is not publicly available and has been denied under the Freedom of Information Act. (An appeal is pending.) But a previous list dated 1971 and obtained by researcher Michael Ravnitzky is available here (pdf).
Most of the listed technology areas are closely related to military applications. But some of them range more widely.
Thus, the 1971 list indicates that patents for solar photovoltaic generators were subject to review and possible restriction if the photovoltaics were more than 20% efficient. Energy conversion systems were likewise subject to review and possible restriction if they offered conversion efficiencies “in excess of 70-80%.”
One may fairly ask if disclosure of such technologies could really have been “detrimental to the national security,” or whether the opposite would be closer to the truth. One may further ask what comparable advances in technology may be subject to restriction and non-disclosure today. But no answers are forthcoming, and the invention secrecy system persists with no discernible external review.
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.