Rep. Frank Wolf (R-VA) placed two “sensitive but unclassified” (SBU) State Department documents in the Congressional Record last week, illustrating the informal, non-binding character of this information control marking.
Rep. Wolf took to the House floor to express his views on the Council on American-Islamic Relations (CAIR), an advocacy organization that he said had unacceptable links to terrorist groups. CAIR has disputed such allegations.
But what is of interest here is Rep. Wolf’s willingness to introduce two State Department cables that are specifically marked “sensitive” and “SBU” and to place them in the public record. Doing so might annoy the State Department and violate unofficial norms of confidentiality, but it breaks no law.
The problematic aspect of SBU and similar labels is that anyone can mark anything “sensitive” or “for official use only” for any reason. The system is completely unregulated. But the flip side is that records bearing such markings are not rigorously protected and in fact are often openly distributed.
As the government moves to replace all kinds of SBU markings with a more uniform “controlled unclassified information” (CUI) system, the expectation is that the standards for applying controls on sensitive but unclassified records will be more clearly articulated, limited and enforced. By the same token, however, the freewheeling disclosure of such records may grind to a halt. It’s hard to know in advance if the benefits in terms of public access to government information will exceed the costs.
Americans are paying too much for almost everything, because the United States has long treated its trucking industry as an artifact to be preserved rather than as an opportunity for innovation.
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.