Militaries seek to harness artificial intelligence for decision advantage. Yet AI systems introduce a new source of uncertainty in the likelihood of technical failures. Such failures could interact with strategic and human factors in ways that lead to miscalculation and escalation in a crisis or conflict. Harnessing AI effectively requires managing these risk trade-offs by reducing the likelihood, and containing the consequences of, AI failures.

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 using different tools, data modalities, and resources, which adds to the diversity of their assessment. Thus, a collection of approaches and processes is needed to cover a wide range of AI products, tools, services, and resources.

China’s government has pushed the country’s technology and financial firms to expand abroad, and Southeast Asia’s growing economies — and AI companies — offer promising opportunities. This report examines the scope and nature of Chinese investment in the region. It finds that China currently plays a limited role in Southeast Asia’s emerging AI markets outside of Singapore and that Chinese investment activity still trails behind that of the United States. Nevertheless, Chinese tech companies, with support from the Chinese government, have established a broad range of other AI-related linkages with public and commercial actors across Southeast Asia.

CSET submitted the following comment in response to the National Institute for Standards and Technology's second draft of its AI Risk Management Framework.

Through the Quad forum, the United States, Australia, Japan and India have committed to pursuing an open, accessible and secure technology ecosystem and offering a democratic alternative to China’s techno-authoritarian model. This report assesses artificial intelligence collaboration across the Quad and finds that while Australia, Japan and India each have close AI-related research and investment ties to both the United States and China, they collaborate far less with one another.

Problems of AI safety are the subject of increasing interest for engineers and policymakers alike. This brief uses the CSET Map of Science to investigate how research into three areas of AI safety — robustness, interpretability and reward learning — is progressing. It identifies eight research clusters that contain a significant amount of research relating to these three areas and describes trends and key papers for each of them.

​​This Classifying AI Systems Interactive presents several AI system classification frameworks developed to distill AI systems into concise, comparable and policy-relevant dimensions. It provides key takeaways and framework-specific results from CSET’s analysis of more than 1,800 system classifications done by survey respondents using the frameworks. You can explore the frameworks and example AI systems used in the survey, and even take the survey.

This paper is the fourth 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 these systems work safely and reliably. The first paper in the series, “Key Concepts in AI Safety: An Overview,” outlined three categories of AI safety issues—problems of robustness, assurance, and specification—and the subsequent two papers described problems of robustness and assurance, respectively. This paper introduces specification as a key element in designing modern machine learning systems that operate as intended.

This brief explores the development and testing of artificial intelligence system classification frameworks intended to distill AI systems into concise, comparable and policy-relevant dimensions. Comparing more than 1,800 system classifications, it points to several factors that increase the utility of a framework for human classification of AI systems and enable AI system management, risk assessment and governance.

Allies of the United States have begun to develop their own policy approaches to responsible military use of artificial intelligence. This issue brief looks at key allies with articulated, emerging, and nascent views on how to manage ethical risk in adopting military AI. The report compares their convergences and divergences, offering pathways for the United States, its allies, and multilateral institutions to develop common approaches to responsible AI implementation.