Recent news stories on security clearances (like these from the Christian Science Monitor and NPR) cite data from 2015 regarding the number of persons cleared for access to classified information (4.2 million at that time).
Why aren’t more current numbers being cited?
More recent information has already been compiled in an annual report to Congress that was completed in October 2017. But its release to the public has been delayed indefinitely by an internal intelligence community dispute over the classification status or sensitivity of some of the more detailed reporting on individual agency statistics that are contained in the report.
In fact, the same detailed reporting was provided in the 2015 report and the same dispute over publication arose. But at that time, Obama Administration intelligence officials told security officers in effect to “knock it off” and to just release the report, which they did in June 2016.
The public disclosure of security clearance data was one of dozens of fundamental changes to national security information policy that were made during the Obama Administration to promote greater transparency. Although the annual report on security clearances was required by Congress (in the FY 10 intelligence authorization act), its public disclosure was a choice made by the Obama Administration. Now a different choice is being made.
A FOIA request for release of the latest report on security clearances is pending.
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.