Comments on AI Risk Management Framework Second Draft

This document is a response to the National Institute of Standards and Technology's (NIST) request for comments on its second draft of the Artificial Intelligence Risk Management Framework (AI RMF). It outlines Neo4j's recommendations for improving the draft, emphasizing the importance of measuring accuracy for subgroups of data, adopting a systems approach to AI safety, and enhancing the language and usability of the framework's function tables. The response details the characteristics of trustworthy AI, including validity, reliability, safety, and the need for a human-centered understanding of AI systems. It discusses the implications of Simpson’s Paradox in data analysis and the necessity of continuous monitoring throughout the AI lifecycle. The document concludes by highlighting the significance of context in AI systems and praises NIST's efforts in developing the guidelines, expressing a desire for continued collaboration in future iterations.