Institutionalizing Innovation in Secrecy Policy
It is possible to imagine all kinds of changes in government secrecy policy that would make the secrecy system smaller, more efficient, more susceptible to error correction, and more attuned to shifting security requirements.
Such changes might include, for example, self-cancelling classification markings, numerical limits on classification activity, broadly distributed oversight and declassification authority, new mechanisms for challenging classification decisions, and so on.
But before any such change could be adopted in practice, it would almost certainly need to be tested and validated for use, particularly if it involved a real departure from current procedures.
A classification policy “test bed” in which a variety of new classification policies could be put into practice on a limited scale would therefore be desirable, and would signify a non-rhetorical commitment to policy change.
It is interesting to note that the need to systematically approach change has been recognized in other national security contexts, which might serve as a model for secrecy reform.
The U.S. Army actually has its own Logistics Innovation Agency whose mission is “to provide innovative solutions for improved operational and tactical logistics readiness.”
The Agency “uses well-defined processes of exploration, discovery, demonstration, and transition to integrate logistics solutions that help prepare the Army for uncertain and complex future operating environments,” according to an updated Army regulation published last week.
Similarly, the U.S. Navy has an Office of Innovation that “promotes, fosters, and develops innovative science, technology, processes and policies that support the Department of the Navy.”
These and similar entities might be persuaded or directed to undertake pilot projects on innovations in national security classification. If successful, such efforts could advance a consensus view of sharply limited secrecy that is more responsive to the public interest in both security and disclosure.
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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.
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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.