The late Kenneth C. Bass, who helped draft the Foreign Intelligence Surveillance Act of 1978 and who was the first Counsel for Intelligence Policy at the Justice Department, later became a critic of its implementation and a proponent of remedial measures.
In a 1994 congressional hearing, he urged lawmakers to introduce elements of an adversarial process into the FISA Court, such as appointing an advocate for the proposed target, so that judges would have a more complete and nuanced record on which to base their decisions to approve surveillance and physical search. See “Amending the Foreign Intelligence Surveillance Act” (pdf), hearing before the House Permanent Select Committee on Intelligence, July 14, 1994.
In 2002 testimony, he reiterated this proposal and told a Senate Judiciary Committee hearing on the FISA process that it should also “obtain more information and make it public.” Neither recommendation was acted upon, and the efficacy of the FISA as a legal constraint upon intelligence surveillance would soon be diminished by the Bush Administration’s circumvention of its procedural requirements.
Kenneth Bass was remembered in “Justice Official Helped Pen Surveillance Act” by Patricia Sullivan, Washington Post, May 2, 2009.
“Laws never prevent lawlessness,” Mr. Bass said at the 1994 hearing. “But they are designed to check it and give somebody else a second view of what to do with it.”
Kate Martin of the Center for National Security Studies, who also testified at that hearing, told Committee members that Mr. Bass’ proposal for an adversarial review of FISA applications was unobjectionable but that by itself it would not cure the constitutional infirmities of FISA.
“The Constitution is not a perfect information-gathering system for the government,” Ms. Martin said.
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.