The amount of money appropriated for U.S. intelligence increased in 2016 by about 5 percent to a total of $70.7 billion, up from $66.8 billion the year before.
The total includes FY 2016 appropriations for both the National Intelligence Program (NIP) and the Military Intelligence Program (MIP), which were officially disclosed on October 28, as they have been each year since 2007.
Opponents of intelligence budget disclosure had argued for decades that release of the total budget figures would lead inexorably to further uncontrolled disclosures.
In 1976, former Director of Central Intelligence James Schlesinger told the Church Committee that “One of the problems here is the camel’s nose under the edge of the tent, and I think that that is the fundamental problem in the area. There are very few people who can articulately argue that the publication of those [budget] figures in and of themselves, if it stopped there, would be harmful. The argument is that then the pressure would build up to do something else, that once you have published for example the… budget, that the pressures would build up to reveal the kinds of systems that are being bought for that money, and it is regarded as the first step down a slippery slope for those who worry about those kinds of things.”
But that concern about a “slippery slope” appears to have been refuted in practice, and — aside from unauthorized disclosures — additional budget secrets have been effectively preserved.
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.