The executive branch is reconfiguring its approach to vetting individuals for access to sensitive information and granting them security clearances in an attempt to modernize and improve its procedures, according to a new quarterly report.
“The Insider Threat and Security Clearance Reform (ITSCR) Cross Agency Priority (CAP) Goals have been re-baselined so that they are aligned with the new enterprise-wide focus . . . and its four work streams (Trusted Workforce, Modern Vetting, Secure and Modern Mission-Capable IT, and Continuous Performance Improvement) for modernizing the SSC [security, suitability/fitness, and credentialing] mission over the next five years.” See the Quarterly Progress Update on Insider Threat and Security Clearance Reform, FY2016 Quarter 3, September 2016.
Translated out of bureaucratic jargon, this statement… still remains obscure and hard to understand. But at the least, it implies a determination that existing arrangements are unsatisfactory and that they require adjustment.
Among other steps, the latest Quarterly Update says that by December of this year, the Office of the Director of National Intelligence will “Establish a policy that requires the national security population to report information of security concern to the proper authorities in a timely manner.” The exact nature of such a requirement and its likely effect on “the national security population” remain to be seen.
Though security clearance “reform” of some kind has been underway for many years, the recent arrest of former NSA contractor Harold T. Martin III on suspicion of theft and retention of classified information suggests that room for improvement still exists. (“NSA case highlights growing concerns over insider threats” by Christian Davenport, Washington Post, October 6).
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.