This explainer defines criteria for effective AI Incident Collection and identifies tradeoffs between potential reporting models: mandatory, voluntary, and citizen reporting.

CSET submitted the following comment in response to a Request for Information (RFI) from the National Science Foundation (NSF) about the development of the newly established Technology, Innovation, and Partnerships (TIP) Directorate, in accordance with the CHIPS and Science Act of 2022.

Real-world harms caused by the use of AI technologies are widespread. Tracking and analyzing them improves our understanding of the variety of harms and the circumstances that lead to their occurrence once AI systems are deployed. This report presents a standardized conceptual framework for defining, tracking, classifying, and understanding harms caused by AI. It lays out the key elements required for the identification of AI harm, their basic relational structure, and definitions without imposing a single interpretation of AI harm. The brief concludes with an example of how to apply and customize the framework while keeping its modular structure.

Process frameworks provide a blueprint for organizations implementing responsible artificial intelligence (AI), but the sheer number of frameworks, along with their loosely specified audiences, can make it difficult for organizations to select ones that meet their needs. This report presents a matrix that organizes approximately 40 public process frameworks according to their areas of focus and the teams that can use them. Ultimately, the matrix helps organizations select the right resources for implementing responsible AI.

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.

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

CSET Senior Fellow Andrew Lohn testified before the House of Representatives Homeland Security Subcommittee on Cybersecurity, Infrastructure Protection, and Innovation at a hearing on "Securing the Future: Harnessing the Potential of Emerging Technologies While Mitigating Security Risks." Lohn discussed the application of AI systems in cybersecurity and AI’s vulnerabilities.

CSET Senior Fellow Andrew Lohn testified before the House of Representatives Science, Space and Technology Subcommittee on Investigations and Oversight and Subcommittee on Research and Technology at a hearing on "Securing the Digital Commons: Open-Source Software Cybersecurity." Lohn discussed how the United States can maximize sharing within the artificial intelligence community while reducing risks to the AI supply chain.

CSET Senior Fellow Andrew Lohn testified before the U.S. Senate Armed Services Subcommittee on Cybersecurity hearing on artificial intelligence applications to operations in cyberspace. Lohn discussed AI's capabilities and vulnerabilities in cyber defenses and offenses.