Georgetown University’s Center for Security and Emerging Technology and Beeck Center for Social Impact and Innovation, together with the Georgetown Law Institute for Technology Law and Policy (Tech Institute), led a series of invite-only roundtables over the course of 2024 to grapple with the legal liability questions that artificial intelligence poses, examine AI’s potential to transform government services, and consider how the government can better attract and use AI talent. This resulting report was authored in 2024 after those discussions and is the first installment of a four-part series.

As new advanced AI systems roll out, there is widespread disagreement about malicious use risks. Are bad actors likely to misuse these tools for harm? This report presents a simple framework to guide the questions researchers ask—and the tools they use—to evaluate the likelihood of malicious use.

In response to the Office of Science and Technology Policy's request for input on an AI Action Plan, CSET provides key recommendations for advancing AI research, ensuring U.S. competitiveness, and maximizing benefits while mitigating risks. Our response highlights policies to strengthen the AI workforce, secure technology from illicit transfers, and foster an open and competitive AI ecosystem.

The United States leads the world in space launch by nearly every measure: number of launches, total mass to orbit, satellite count, and more. SpaceX’s emergence has provided regular, reliable, and relatively affordable launches to commercial and national security customers. However, today’s market consolidation coupled with the capital requirements necessary to develop rockets may make it difficult for new competitors to break in and keep the space launch market dynamic.

This report proposes an approach to systematically identify promising research using big data and analyze that research’s potential impact through structured engagements with subject-matter experts. The methodology offers a structured way to proactively monitor the research landscape and inform strategic R&D priorities.

Automation bias is a critical issue for artificial intelligence deployment. It can cause otherwise knowledgeable users to make crucial and even obvious errors. Organizational, technical, and educational leaders can mitigate these biases through training, design, and processes. This paper explores automation bias and ways to mitigate it through three case studies: Tesla’s autopilot incidents, aviation incidents at Boeing and Airbus, and Army and Navy air defense incidents.

Sam Bresnick testified before the Senate Judiciary Subcommittee on Privacy, Technology, and the Law regarding tech companies' ties to China and their implications in a future conflict scenario.

Artificial intelligence models have become increasingly adept at generating computer code. They are powerful and promising tools for software development across many industries, but they can also pose direct and indirect cybersecurity risks. This report identifies three broad categories of risk associated with AI code generation models and discusses their policy and cybersecurity implications.

Computer scientists have long sought to build systems that can actively and autonomously carry out complicated goals in the real world—commonly referred to as artificial intelligence "agents." Recently, significant progress in large language models has fueled new optimism about the prospect of building sophisticated AI agents. This CSET-led workshop report synthesizes findings from a May 2024 workshop on this topic, including what constitutes an AI agent, how the technology is improving, what risks agents exacerbate, and intervention points that could help.

As critical infrastructure operators and providers seek to harness the benefits of new artificial intelligence capabilities, they must also manage associated risks from both AI-enabled cyber threats and potential vulnerabilities in deployed AI systems. In June 2024, CSET led a workshop to assess these issues. This report synthesizes our findings, drawing on lessons from cybersecurity and insights from critical infrastructure sectors to identify challenges and potential risk mitigations associated with AI adoption.