Too often, affected patients, clinicians, and regulators cannot see how the system works, why a decision was made, or whether meaningful human oversight occurred.
Existing tools from other domains, such as existing robust public engagement processes in drug development, when applied to AI deployment can help strengthen public trust in these systems and enhance perceptions of their legitimacy and the decisions they produce.
With thoughtful policy action, it is still possible to build systems that are fair, transparent, and accountable, and to earn the public trust that will ultimately determine AI’s future. We hope policymakers are ready to act.
Procurement is not merely an administrative function—it is how AI enters government and the first line of defense for responsible AI in the public sector.
Responsible AI starts with who is in the data, who is at the table, whose needs shape the outcome, and who is responsible when it falls short.
There is no question this is a Big Deal. If you are a university or research lab, or aspire to work in one, or are simply an enthusiast of federally-funded research, what’s next will matter.
The emerging federal metascience community is asking fascinating questions that are equally vital for democratic legitimacy: beyond “did this program work” to “how does the federal R&D enterprise itself work, and how could it work better?”
If you’re new to the climate intervention space, welcome! The TL;DR: if we can’t stop the most catastrophic impacts of climate change with current tools quickly enough, then we need a bigger toolbox.
After months of delay, the council tasked by President Trump to review the FEMA released its final report. Our disaster policy nerds have thoughts.
FAS and FLI partnered to build a series of convenings and reports across the intersections of artificial intelligence (AI) with biosecurity, cybersecurity, nuclear command and control, military integration, and frontier AI governance. This project brought together leaders across these areas and created a space that was rigorous, transpartisan, and solutions-oriented to approach how we should think about how AI is rapidly changing global risks.
Investment should instead be directed at sectors where American technology and innovation exist but the infrastructure to commercialize them domestically does not—and where the national security case is clear.
AI is already consequential, but its future trajectory remains contested. Policymakers should make their assumptions explicit, focus on what can be shaped rather than what can be perfectly predicted, and build institutions that can learn and respond as evidence changes.