CSET submitted the following comment in response to a Request for Information (RFI) from the Department of Commerce regarding 89 FR 27411.

How to govern artificial intelligence is a concern that is rightfully top of mind for lawmakers and policymakers.To govern AI effectively, regulators must 1) know the terrain of AI risk and harm by tracking incidents and collecting data; 2) develop their own AI literacy and build better public understanding of the benefits and risks; and 3) preserve adaptability and agility by developing policies that can be updated as AI evolves.

Policy and research communities strive to mitigate AI harm while maximizing its benefits. Achieving effective and trustworthy AI necessitates the establishment of a shared language. The analysis of policies across different countries and research literature identifies consensus on six critical concepts: accountability, explainability, fairness, privacy, security, and transparency.

This paper is the fifth 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. This paper explores the opportunities and challenges of building AI systems that “know what they don’t know.”

President Biden's October 2023 executive order prioritizes the governance of artificial intelligence in the federal government, prompting the urgent creation of AI risk management standards and procurement guidelines. Soon after the order's signing, the Office of Management and Budget issued guidance for federal departments and agencies, including minimum risk standards for AI in federal contracts. Similar to cybersecurity, procurement rules will be used to enforce AI development best practices for federal suppliers. This report offers recommendations for implementing AI risk management procurement rules.

China’s brain-computer interface research has two dimensions. Besides its usual applications in neuropathology, China is extending the benefits of BCI to the general population, aiming at enhanced cognition and a “merger” of natural and artificial intelligence. This report, authored in collaboration with researchers from the Department of War Studies at King’s College London uses bibliometric analysis and expert assessment of technical documents to evaluate China’s BCI, and conclude that the research is on track to achieve its targets.

Artificial Intelligence incidents have been occurring with the rapid advancement of AI capabilities over the past decade. However, there is not yet a concerted policy effort in the United States to monitor, document, and aggregate AI incident data to enhance the understanding of AI-related harm and inform safety policies. This report proposes a federated approach consisting of hybrid incident reporting frameworks to standardize reporting practices and prevent missing data.

On February 2, 2024, CSET's Assessment and CyberAI teams submitted a response to NIST's Request for Information related to the Executive Order Concerning Artificial Intelligence (88 FR 88368). In the submission, CSET compiles recommendations from six CSET reports and analyses in order to assist NIST in its implementation of AI Executive Order requirements.

CSET submitted the following comment in response to a Request for Comment (RFC) from the Office of Management and Budget (OMB) about a draft memorandum providing guidance to government agencies regarding the appointment of Chief AI Officers, Risk Management for AI, and other processes following the October 30, 2023 Executive Order on AI.

Standards enable good governance practices by establishing consistent measurement and norms for interoperability, but creating standards for AI is a challenging task. The Center for Security and Emerging Technology and the Center for a New American Security hosted a series of workshops in the fall of 2022 to examine standards development in the areas of finance, worker safety, cybersecurity, sustainable buildings, and medical devices in order to apply the lessons learned in these domains to AI. This workshop report summarizes our findings and recommendations.