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.

The U.S. Army’s 18th Airborne Corps can now target artillery just as efficiently as the best unit in recent American history—and it can do so with two thousand fewer servicemembers. This report presents a case study of how the 18th Airborne partnered with tech companies to develop, prototype, and operationalize software and artificial intelligence for clear military advantage. The lessons learned form recommendations to the U.S. Department of Defense as it pushes to further develop and adopt AI and other new technologies.

The U.S. government has an opportunity to seize strategic advantages by working with the remote sensing and data analysis industries. Both grew rapidly over the last decade alongside technology improvements, cheaper space launch, new investment-based business models, and stable regulation. From new sensors to new orbits, the intelligence community and regulators have recognized these changes and opportunities—the U.S. Department of Defense, NASA, and other agencies should follow suit.

While the effects of the U.S. Department of Defense’s broad investments in research and development go far beyond what is publicly disclosed, authors affiliated with the DOD do publish papers about their research. This analysis examines more than 100,000 papers by DOD-affiliated authors since 2000 and offers insight into the patterns of research publication and collaboration by the DOD.

China’s leadership believes that artificial intelligence will play a central role in future wars. However, the author's comprehensive review of dozens of Chinese-language journal articles about AI and warfare reveals that Chinese defense experts claim that Beijing is facing several technological challenges that may hinder its ability to capitalize on the advantages provided by military AI. This report outlines these perceived barriers and identifies several technologies that Chinese experts believe may help the country develop and deploy military AI-enabled systems.

U.S. technology companies have become important actors in modern conflicts, and several of them have meaningfully contributed to Ukraine’s defense. But many of these companies are deeply entangled with China, potentially complicating their decision-making in a potential Taiwan contingency.

How can policymakers credibly reveal and assess intentions in the field of artificial intelligence? Policymakers can send credible signals of their intent by making pledges or committing to undertaking certain actions for which they will pay a price—political, reputational, or monetary—if they back down or fail to make good on their initial promise or threat. Talk is cheap, but inadvertent escalation is costly to all sides.

Australia, Canada, Japan, the United Kingdom, and the United States emphasize principles of accountability, explainability, fairness, privacy, security, and transparency in their high-level AI policy documents. But while the words are the same, these countries define each of these principles in slightly different ways that could have large impacts on interoperability and the formulation of international norms. This creates, what we call the “Inigo Montoya problem” in trustworthy AI, inspired by "The Princess Bride" movie quote: “You keep using that word. I do not think it means what you think it means.”

This data brief uses procurement records published by the U.S. Department of Defense and China’s People’s Liberation Army between April and November of 2020 to assess, and, where appropriate, compare what each military is buying when it comes to artificial intelligence. We find that the two militaries are prioritizing similar application areas, especially intelligent and autonomous vehicles and AI applications for intelligence, surveillance and reconnaissance.