The Director of National Intelligence shall “review the system by which the Government classifies and declassifies information” and shall “develop recommendations… to make such system a more effective tool… and to support the appropriate declassification of information.”
That’s just one of the many requirements included in the Fiscal Year 2017 Intelligence Authorization Act (in section 708) that was approved by the House of Representatives on November 30, following negotiations with the Senate.
The House and Senate Intelligence Committees also produced an Explanatory Statement that presents extensive “unclassified congressional direction” on all kinds of intelligence policy matters high and low.
The joint Statement, included in the Congressional Record, notably adopts House language on reforming the pre-publication review requirement that current and former intelligence community employees (and certain others) must comply with. The Statement requires the DNI to “issue an IC-wide policy regarding pre-publication review” within 180 days that includes various specified elements that should improve the timeliness, clarity, and fairness of the review process.
The intelligence bill was crafted in response to Obama Administration policies and, in all likelihood, in anticipation of a Hillary Clinton Administration. But assuming that it is enacted into law, it will come into full effect in a Trump Administration of uncertain character and composition.
“There are many unknowns about the incoming administration, particularly how it will utilize and interact with the IC,” said Rep. Adam Schiff (D-CA), the Ranking Member of the House Intelligence Committee.
“It is now more important than ever that we give the IC the tools it needs to keep us safe and provide the necessary oversight required to ensure that they act in a manner consistent with our values and at all times,” he said on the House floor.
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.