America's National Science Board (NSB) "was established in 1950 to guide the governance of the National Science Foundation," writes the Washington Post, "in an unusual structure within the federal government that echoes the setup of a company board in the private sector. It helps guide an agency that operates Antarctic research stations, telescopes, a fleet of research vessels and supports basic science research in laboratories across the United States." (NSF research has helped evolve the technology used in MRIs, cellphones and LASIK eye surgery.)

But yesterday President Trump fired all 24 members of the National Science Board (NSB), the body that oversees the National Science Foundation (NSF), reports Science magazine: In addition to advising the administration and Congress on national science policy, it has statutory authority to oversee the actions of the $9-billion NSF, setting policy and approving large expenditures. Its presidentially appointed members, typically prominent academics and industry leaders, serve 6-year terms, with eight members chosen every 2 years....

Keivan Stassun, one of the dismissed board members, says the mass firing is the latest indication that the White House is ignoring the board's authority and dictating policies at NSF, which has been without a permanent director since Sethuraman Panchanathan resigned exactly one year ago. Stassun, an astrophysicist at Vanderbilt University who was appointed to the board in 2022, thinks the board's public criticism in May 2025 of Trump's proposed 55% cut to NSF's current budget — which Congress ultimately ignored — antagonized the administration. "Maybe one way to say it from the administration's perspective," Stassun says, "is that this group of presidential appointees was advising the Congress to not follow the president's wishes."
The Washington Post adds that "The White House did not immediately respond to inquiries about why the members were terminated."

It's the U.S. government's main link to the AI industry, reports The Washington Post, working to assess national security risks of new models like Anthropic's "Mythos".

To run it they'd hired Collin Burns, who'd worked at OpenAI and then Anthropic. But Burns started work Monday at the Center for AI Standards and Innovation — and then "was pushed out Thursday by the White House, according to the people, who spoke on the condition of anonymity to describe private conversations." Officials were concerned about Burns having worked at the AI company, which has fought bitterly with the Trump administration in recent months, according to one of the people and another person. That person said some senior figures at the White House had not been briefed on Burns's selection in advance... The new pick was Chris Fall, a scientist with a long career spanning the federal government and academia. Burns had been asked to resign that afternoon, according to one of the people familiar with the situation...

Dean Ball, a former Trump administration AI adviser, said on social media that Burns had given up valuable Anthropic stock and moved across the country to take the government position, and had been "rewarded by his country with a punch in the face." "Obviously what happened is Burns was bumped because of his association with Anthropic," Ball wrote. "A dumb but predictable own goal."

The Free Software Foundation's Licensing and Compliance Manager published a blog post this week to explicitly state that"Responsible AI" Licenses (RAIL) are nonfree and unethical. The licenses restrict AI and ML software "from being used in a specific list of harmful applications," according to the license's web site, "e.g. in surveillance and crime prediction." (The license's steering committee is volunteers from multiple academic institutions.)

But even though Responsible AI licenses are marketed as addressing ethical challenges, the FSF argues "they do not require anything that is really necessary for users to control their computing done with machine learning, including: complete training inputs, training configuration settings, trained model, or — last, but not least — the source code of software used for training, testing, and running tools based on machine learning." Thus, RAILed machine learning can be, and most probably will be, unethical. Use restrictions do not prevent these licenses from being used to exercise power over users...

RAIL contribute to unethical marketing of machine learning, again under the disguise of morally-loaded restrictions they purport to enforce. If we want software to help decrease social injustice, we should oppose licenses that restrict how software can be used. We should focus on effective ways of addressing injustices: government and community support for freedom-respecting tools and services; releasing programs under strong copyleft licenses; and entrusting copyrights to organizations that have the resources to enforce copyleft.

Software freedom must be defended, not denied. More specifically, the more free software is out there, the more likely people will collaborate on tools and services that do not pose moral dangers and help solve existing ones. Free software also makes it more likely that users have real choices when looking for freedom-respecting ethical programs and tools based on machine learning. Denying people the freedom to a particular program, as RAIL or similar licenses would have it, prevents them from using such program for the common good.