The House of Representatives yesterday passed the Overclassification Reduction Act, a bill that is intended to help reduce inappropriate classification of information in government.
The bill would require the National Archivist to develop regulations to help combat overclassification. The bill would mandate increased accountability for classification actions, with incentives for challenging improper classification and penalties for abuse of classification authority. Importantly, it would require agency inspectors general to perform periodic audits of classification activity to ensure compliance with classification standards.
While the bill represents a welcome expression of congressional interest in overclassification, its proposed solution does not seem carefully adapted to the problem.
“The problem of overclassification is government-wide and it demands a government-wide solution,” said Rep. Henry Waxman (D-CA), who introduced the bill along with Rep. Tom Davis (R-VA).
But that is unlikely to be true, because it presumes that overclassification is a uniform phenomenon across the government, which is not the case. Overclassification at the CIA is not the same as overclassification at the Pentagon or the State Department. Not only do these agencies have different institutional cultures, their classification policies revolve around different sets of security concerns, and they are implemented through distinct sets of procedures.
A government-wide regulation like the 2003 implementing directive issued by the Information Security Oversight Office can set important parameters for classification duration, classifier training, document marking, and so forth. But that directive has not been an effective vehicle for reversing or combating overclassification.
An alternate approach to the problem will be described in Secrecy News next week.
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.