Building on existing data and privacy efforts, the White House and federal science agencies should collaborate to develop and implement clear standards for research data privacy across the data management and sharing life cycle.
The Federation of American Scientists (FAS) announced today its participation in the new AI Safety Institute Consortium (AISIC) launched by the National Institute of Standards and Technology (NIST)
The USPTO should incorporate open source hardware certification databases into the library of resources to search for prior art, and create guidelines and training to build agency capacity for evaluating open source prior art.
Federal agencies should take coordinated action to ensure that data sharing policies incentivize high-quality data management and sharing plans.
All agencies that fund research should require that resulting publications include a Software Bill of Materials (SBOM) listing the software used in the research.
The National Institutes of Health should form an Office of Co-Production in the Office of the Director to ensure meaningful public engagement and rebuild trust.
Federal grantmakers should establish a default expectation that hardware developed as part of federally supported research be released as open hardware.
To build on existing federal efforts supporting scientific rigor and integrity, funding agencies should study and pilot new programs to incentivize researchers’ engagement in credibility-enhancing practices that are presently undervalued in the scientific enterprise.
Russia is in the midst of a decades-long nuclear force modernization program intended to replace Soviet-era missiles, aircraft, and submarines with new systems.
In scientific work in the service of agency missions, the federal government should use and contribute to open source hardware.
To enhance transparency, encourage collaboration, and optimize public-good impacts, funding agencies should allow researchers to make grant proposals publicly available.
Federal agencies should form Data Collaboratives in which staff and members of the public engage in mutual learning about available datasets and their affordances for clarifying policy problems.