Pressure to adopt “sensitive but unclassified” control markings on information that does not qualify for classification is growing, along with opposition to such controls, among some academic researchers who study terrorism-related topics. See “Scientific Openness: Should Academics Self-Censor Their Findings on Terrorism?” by Yudhijit Bhattacharjee, Science, May 19.
“The secrecy that has become such a hallmark of the Bush administration did not begin with Sept. 11, as the White House often suggests. It began in the earliest days of January 2001, as the administration was taking shape,” according to a National Public Radio account. See “From the Start, Bush White House Kept Secrets” by Don Gonyea, NPR Weekend Edition, May 21.
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.