This paper is the first installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure… Read More

Artificial intelligence-enabled systems are transforming society and driving an intense focus on what policy and technical communities can do to ensure that those systems are trustworthy and used responsibly. This analysis draws on prior work… Read More

When the technology and policy communities use terms associated with trustworthy AI, could they be talking past one another? This paper examines the use of trustworthy AI keywords and the potential for an “Inigo Montoya… Read More

As modern machine learning systems become more widely used, the potential costs of malfunctions grow. This policy brief describes how trends we already see today—both in newly deployed artificial intelligence systems and in older technologies—show… Read More

Recent discussions of AI have focused on safety, reliability, and other risks. Lost in this debate is the real need to secure AI against malicious actors. This blog post applies lessons from traditional cybersecurity to… Read More

Artificial intelligence is so diverse in its range that no simple one-size-fits-all assessment approach can be adequately applied to it. AI systems have a wide variety of functionality, capabilities, and outputs. They are also created… Read More

As the U.S. military integrates artificial intelligence into its systems and missions, there are outstanding questions about the role of trust in human-machine teams. This report examines the drivers and effects of such trust, assesses… Read More

With the rapid integration of AI into our daily lives, we must all learn when and whether to trust the technology, understand its capabilities and limitations, and adapt as these systems — and our functional… Read More