U.S. intelligence agencies are anticipating budget reductions of billions of dollars, said Director of National Intelligence James Clapper yesterday. He said he had just submitted a draft budget to OMB (presumably for FY 2013) that involved “double digit” cuts to the intelligence budget over ten years. See “U.S. Spies Facing Tens of Billions in Budget Cuts” by Sharon Weinberger, Wired Danger Room, October 17.
“In the last 10 years,… all we had to do essentially was preside over handing out more money and more people every year,” DNI Clapper told a joint hearing of the House and Senate Intelligence Committees last month.
But “now we’re in a ‘we’re-running-out-of-money-so-we-must-begin-to-think’ mode,” he said. “I think that is serving as the stimulus, if you will, to do some more creative thinking. I think this would do wonders in terms of saving money, efficiency, and promoting integration.”
“Everything we do in intelligence… is not of equal merit. Some things are more valuable than others, particularly as we look to the future. I think it’s very important to try to protect that valuable and most valuable resource we have, which is our people. We must continue some way of hiring every year, which we didn’t do in many cases during that seven-year hiatus period [in the 1990s]. We must try to sustain healthy R&D for the future. And I think we have to be rather cold-hearted and objective about the real contribution the various systems make. So that’s kind of the approach we’re going to take,” DNI Clapper told Congress last month.
“I don’t want anyone to be under the mistaken impression that we are going to sustain all the capabilities we have today, because we’re not,” he said.
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.