🚨GENERATIVE AI & HUMAN RIGHTS: the UN published the paper "Taxonomy of Human Rights Risks Connected to Generative AI," and it's a must-read for everyone in AI. Here's why: ➡️The taxonomy supplements the UN B-Tech Project’s foundational paper on generative AI and examines how generative AI may negatively impact human rights (providing real-world examples for each right). ➡️These are the rights covered: ➵ Freedom from Physical and Psychological Harm ➵ Right to Equality Before the Law and to Protection against Discrimination ➵ Right to Privacy ➵ Right to Own Property ➵ Freedom of Thought, Religion, Conscience and Opinion ➵ Freedom of Expression and Access to Information ➵ Right to Take Part in Public Affairs ➵ Right to Work and to Gain a Living ➵ Rights of the Child ➵ Rights to Culture, Art and Science ➡️For each of these rights, the report covers: ➵ a summary of why the right is at risk from the development or use of generative AI; ➵ a selected list of key international human rights law articles pertaining to the right; ➵ a list of examples in which generative AI may threaten the right. ➡️This is an extremely important paper to anyone concerned with AI's real-world risks and harms. Read the full document below. ➡️To learn more about the latest developments in AI policy & regulation, subscribe to my newsletter (link below).

🚨BREAKING: @OECD revises its AI principles. What you need to know: ➡️According to the official release: "In response to recent developments in AI technologies, notably the emergence of general-purpose and generative AI, the updated Principles more directly address AI-associated challenges involving privacy, intellectual property rights, safety, and information integrity." ➡️Key revisions include: ➵ "Addressing safety concerns, so that if AI systems risk causing undue harm or exhibit undesired behaviour, robust mechanisms and safeguards exist to override, repair, and/or decommission them safely; ➵ Reflecting the growing importance of addressing mis- and disinformation, and safeguarding information integrity in the context of generative AI; ➵ Emphasising responsible business conduct throughout the AI system lifecycle, involving co-operation with suppliers of AI knowledge and AI resources, AI system users, and other stakeholders; ➵ Clarifying the information regarding AI systems that constitute transparency and responsible disclosure; ➵ Explicitly referencing environmental sustainability, a concern that has grown considerably in importance over the past five years; ➵ Underscoring the need for jurisdictions to work together to promote interoperable governance and policy environments for AI, as the number of AI policy initiatives worldwide surges." ➡️Link to the revised principles below. ➡️To stay up to date with AI policy, reports & laws, subscribe to my newsletter.

🚨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.