In a thought-provoking op-ed featured in Lawfare, CSET's Zachary Arnold and Micah Musser delve into the dynamic discourse surrounding the regulation of artificial intelligence (AI). Read More

In his op-ed featured in Defense One, Jaret Riddick discusses the need for the Pentagon to leverage existing laws and new metrics to enhance the research capacity of historically Black colleges and universities (HBCUs) in the United States. Read More

The much-anticipated National Cyber Workforce and Education Strategy (NCWES) provides a comprehensive set of strategic objectives for training and producing more cyber talent by prioritizing and encouraging the development of more localized cyber ecosystems that serve the needs of a variety of communities rather than trying to prescribe a blanket policy. This is a much-needed and reinvigorated approach that understands the unavoidable inequities in both cyber education and workforce development, but provides strategies for mitigating them. In this blog post, we highlight key elements that could be easily overlooked. Read More

Drawing from prior CSET research, this blog post describes different domestic and international initiatives the Chinese government and companies are pursuing to shore up investment in AI and meet China’s strategic objectives, as well as indicators to track their future trajectories. Read More

In his op-ed featured in Breaking Defense, CSET's Sam Bresnick from offers a deep dive into China's remarkable progress in bolstering space resilience, with a specific focus on tactically responsive space launch (TRSL). Read More

In celebration of Disability Pride Month, the CSET Inclusion Alliance invited guest speaker Linnea Lassiter to shed light on the intersection of technology policy and people with disabilities. Lassiter's insights aimed to encourage learning from, supporting, and celebrating the disabled community. Read More

Large language models (LLMs) could potentially be used by malicious actors to generate disinformation at scale. But how likely is this risk, and what types of economic incentives do propagandists actually face to turn to LLMs? New analysis uploaded to arXiv and summarized here suggests that it is all but certain that a well-run human-machine team that utilized existing LLMs (even open-source ones that are not cutting edge) would save a propagandist money on content generation relative to a human-only operation. Read More

How can we measure the reliability of machine learning systems? And do these measures really help us predict real world performance? A recent study by the Stanford Intelligent Systems Laboratory, supported by CSET funding, provides new evidence that models may perform well on certain reliability metrics while still being unreliable in other ways. This blog post summarizes the study’s results, which suggest that policymakers and regulators should not think of “reliability” or “robustness” as a single, easy-to-measure property of an AI system. Instead, AI reliability requirements will need to consider which facets of reliability matter most for any given use case, and how those facets can be evaluated. Read More

Two CSET researchers are coauthors for a new multi-organization report about the safety of AI systems led by OpenAI and the Berkeley Risk and Security Lab. The report, published on arXiv, identified six confidence-building measures (CBMs) that could be applied by AI labs to reduce hostility, prevent conflict escalation, and improve trust between parties as it relates to foundation AI models. Read More

CSET has received a lot of questions about LLMs and their implications. But questions and discussions tend to miss some basics about LLMs and how they work. In this blog post, we ask CSET’s NLP Engineer, James Dunham, to help us explain LLMs in plain English. Read More