Correction: An Anomalous Rise in Public Knowledge
Secrecy News last week misquoted a line in President Obama’s inaugural speech. He did not say: “And those of us who manage the public’s knowledge will be held to account….” What he said was “And those of us who manage the public’s dollars will be held to account….”
The erroneous reference to “public knowledge” was also published by the Washington Post, United Press International, and other news outlets. It may have originated with a mistake by the FDCH transcription service.
The text of the inaugural address on the White House web site says “public dollars,” not “public knowledge,” and it is clear from the tape of the speech that that is correct. Thanks to reader LD for questioning the discrepancy.
There must be lots of historic events that were mistakenly transcribed and reported.
“You can’t make an anomalous rise twice,” said J. Robert Oppenheimer, according to the official record of his momentous hearing before the Atomic Energy Commission in 1954.
But what Oppenheimer actually said was “You can’t make an omelet rise twice” (as noted by Philip M. Stern). Oh well.
The Oppenheimer case is to be reviewed once again in the latest episode of PBS’s American Experience tonight.
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.