🚨BREAKING: US @NIST publishes 1st draft of its "AI Risk Management Framework: Generative AI Profile." Important information & quotes: ➡️This is a comprehensive document that contains an overview of risks unique to or exacerbated by generative AI (GAI) and an extensive list of actions to manage GAI's risks. ➡️ It highlights the following risks: ➵ CBRN Information ➵ Confabulation ➵ Dangerous or Violent Recommendations ➵ Data Privacy ➵ Environmental ➵ Human-AI Configuration ➵ Information Integrity ➵ Information Security ➵ Intellectual Property ➵ Obscene, Degrading, and/or Abusive Content ➵ Toxicity, Bias, and Homogenization ➵ Value Chain and Component Integration ➡️Quotes: "AI technology can produce varied outputs in multiple modalities and present many classes of user interfaces. This leads to a broader set of AI actors interacting with GAI systems for widely differing applications and contexts of use. These can include data labeling and preparation, development of GAI models, content moderation, code generation and review, text generation and editing, image and video generation, summarization, search, and chat. These activities can take place within organizational settings or in the public domain." (page 63) - "The quality of AI red-teaming outputs is related to the background and expertise of the AI red-team itself. Demographically and interdisciplinarily diverse AI red-teams can be used to identify flaws in the varying contexts where GAI will be used. For best results, AI red-teams should demonstrate domain expertise, and awareness of socio-cultural aspects within the deployment context. AI red teaming results should be given additional analysis before they are incorporated into organizational governance and decision making, policy and procedural updates, and AI risk management efforts." (page 66) - " Provenance data tracking processes can include and assist AI actors across the lifecycle who may not have full visibility or control over the various trade-offs and cascading impacts of early-stage model decisions on downstream performance and synthetic outputs. For example, by selecting a given model to prioritize computational efficiency over accuracy, an AI actor may inadvertently affect provenance tracking reliability." (page 67) ➡️It's a comprehensive and informative document on Generative AI's risk profile. Read it below. ➡️For more information on AI policy & regulation, subscribe to my newsletter.