{"id":975,"date":"2025-06-03T16:37:18","date_gmt":"2025-06-03T16:37:18","guid":{"rendered":"https:\/\/pacific.ai\/staging\/3667\/?p=975"},"modified":"2026-03-12T14:14:19","modified_gmt":"2026-03-12T14:14:19","slug":"pacific-ai-governance-policy-suite-q2-2025-release-notes","status":"publish","type":"post","link":"https:\/\/pacific.ai\/staging\/3667\/pacific-ai-governance-policy-suite-q2-2025-release-notes\/","title":{"rendered":"Pacific AI Governance Policy Suite: Q2 2025 Release Notes"},"content":{"rendered":"
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June 2025<\/strong><\/p>\n

In our March 2025 release (\u201c2025-A\u201d), we consolidated a broad set of laws, regulations, frameworks, and standards into a unified policy suite to guide organizations in developing and deploying AI systems responsibly. With our Q2 2025 update (\u201c2025-B\u201d), we have expanded coverage to include newly enacted legislation, additional healthcare-specific guidance, and several operational policies that reflect evolving best practices in AI governance<\/a>.<\/p>\n

Below, we detail each new source added in the June 2025 release that was not included in March, organized by category.\u00a0These additions reflect our ongoing commitment to transparency, traceability, and compliance\u2014core principles that support effective generative ai governance<\/a> across evolving datasets and models.<\/p>\n

1. Newly Covered Healthcare Guideline Frameworks<\/h2>\n

TRIPOD-AI – Updated Guidance for Reporting Clinical Prediction Models<\/a><\/p>\n

Added in June 2025:<\/strong> TRIPOD-AI builds on the original TRIPOD-LLM checklist by extending transparent reporting requirements to all clinical prediction models that use regression or machine learning methods. It ensures authors disclose data sources, model development procedures, and validation results for both technical and clinical audiences. An AI governance audit<\/a> can be instrumental in ensuring that clinical prediction models adhere to the transparency requirements set by frameworks like TRIPOD-AI and other healthcare-specific guidelines.<\/p>\n

SPIRIT-AI – Guidelines for AI-Related Clinical Trial Protocols<\/a><\/p>\n

Added in June 2025:<\/strong> SPIRIT-AI defines minimum protocol items for randomized controlled trials involving AI interventions. It specifies requirements for model description, input data, training\/validation details, risk management<\/a>, and data monitoring to align with existing SPIRIT guidelines.<\/p>\n

WHO – Generating Evidence for AI-Based Medical Devices<\/a><\/p>\n

Added in June 2025:<\/strong> This WHO framework provides a rigorous pathway for generating evidence on AI\/ML-enabled medical devices, guiding stakeholders through training data curation, validation study design, and post-market evaluation to satisfy regulatory and clinical requirements.<\/p>\n

HAIRA – Advancing Healthcare AI Governance: A Comprehensive Maturity Model<\/a><\/p>\n

Added in June 2025:<\/strong> HAIRA defines a staged maturity model for AI governance in healthcare organizations, assessing domains such as policy alignment, transparency, stakeholder engagement, and risk management. By offering a self-assessment tool, HAIRA helps institutions benchmark and improve their AI governance practices over time.<\/p>\n

TPLC – Total Product Lifecycle Framework for Healthcare AI\/ML<\/a><\/p>\n

Added in June 2025:<\/strong> TPLC adapts the FDA\u2019s Total Product Lifecycle concept to AI\/ML-enabled healthcare solutions. It emphasizes continuous monitoring, real-world performance tracking, and iterative updates across pre-market, deployment, and post-market phases, ensuring AI systems remain safe, effective, and equitable.<\/p>\n

OPTICA – Organizational Perspective Checklist for AI Solutions Adoption<\/a><\/p>\n

Added in June 2025:<\/strong> OPTICA is a practical checklist designed for health systems evaluating AI solutions. It covers organizational readiness (IT infrastructure, workflow integration), clinician training, data<\/a> governance, and outcome measurement, helping healthcare organizations systematically assess non-technical factors critical to successful AI deployment.<\/p>\n

SALIENT – End-to-End Clinical AI Implementation Framework<\/a><\/p>\n

Added in June 2025:<\/strong> SALIENT (Systematic Approaches to Learning, Implementation<\/a>, Evaluation, and Translation) offers guidance for every stage of clinical AI, from early feasibility studies to large-scale rollouts. It emphasizes iterative testing, stakeholder feedback loops, and ongoing governance to ensure AI tools deliver clinical value and remain aligned with patient safety.<\/p>\n

AHRQ & AIMHD Guiding Principles to Address Algorithm Bias<\/a><\/p>\n

Added in June 2025:<\/strong> Developed collaboratively by the Agency for Healthcare Research and Quality (AHRQ) and the Alliance for Integrated Monitoring of Healthcare Disparities (AIMHD), these principles offer concrete steps to identify, measure, and mitigate bias in AI-driven risk prediction and decision support tools, with particular attention to historically marginalized populations.<\/p>\n

\u2018Model Facts\u2019 Label for HTI-1 Compliance by Duke Institute for Health Innovation<\/a><\/p>\n

Added in June 2025:<\/strong> The \u201cModel Facts\u201d label is a standardized one-page document that summarizes key information about AI models\u2014including intended use, population characteristics, performance metrics, and known limitations\u2014to satisfy HHS HTI-1 algorithm transparency requirements and facilitate clinician and patient trust.<\/p>\n

2. New US National Legislation<\/h2>\n

The Take it Down Act (S.146)<\/a><\/p>\n

Added in June 2025:<\/strong> This federal law requires technology platforms to promptly remove certain forms of nonconsensual intimate images and content categorized as \u201crevenge porn.\u201d It mandates takedown procedures, notice to affected individuals, and periodic reporting to Congress on compliance. Although not AI-specific, its removal obligations intersect with AI tools that generate, moderate, or distribute visual content.<\/p>\n

3. New US Federal Regulation<\/h2>\n

Memorandum on Accelerating Federal Use of AI <\/a><\/p>\n

Added in June 2025:<\/strong> This document, issued by the White House, directs all Executive Branch agencies to (a) inventory their existing AI initiatives, (b) establish AI innovation hubs, and (c) adopt common federal AI governance standards\u2014emphasizing transparency, equity, and security. It also outlines funding mechanisms for AI R&D within government.<\/p>\n

Memorandum on Driving Efficient Acquisition of AI in Government <\/a><\/p>\n

Added in June 2025:<\/strong> This memorandum provides agencies with procurement guidance to streamline purchasing of AI\/ML products and services. It encourages the use of shared services, modular licensing, and pre-negotiated contract vehicles to avoid vendor lock-in and ensure continuous security and compliance monitoring.<\/p>\n

4. Newly Covered Acceptable Use Policies by Major Providers<\/h2>\n

OpenAI Usage Policy<\/a><\/p>\n

Defines prohibited content (e.g., hate speech, illicit behavior, disallowed political campaigning), outlines user obligations around data retention and privacy, and details mechanisms for reporting misuse.<\/p>\n

Anthropic Usage Policy<\/a><\/p>\n

Specifies categories of disallowed content (e.g., personal data extraction, weaponization, extreme political persuasion), along with requirements for developers around usage monitoring and red teaming.<\/p>\n

Microsoft Enterprise AI Services Code of Conduct<\/a><\/p>\n

Outlines acceptable uses of Azure OpenAI Service, including prohibitions on illegal, infringing, or malicious applications; mandates adherence to Microsoft\u2019s Responsible AI Standard for fairness, reliability, safety, and privacy.<\/p>\n

AWS Responsible AI Policy<\/a><\/p>\n

Details AWS\u2019s terms for AI\/ML services (e.g., SageMaker), including data classification requirements, customer obligations to secure training data, and a list of disallowed practices (deepfakes, malware generation, surveillance use cases).<\/p>\n

Google Generative AI Prohibited Use Policy<\/a><\/p>\n

Enumerates forbidden use cases for Google\u2019s Vertex AI and Generative AI Studio (e.g., impersonation, disallowed sexual content, targeted political microtargeting), and mandates that developers apply Google\u2019s AI Principles around fairness, privacy, and accountability.<\/p>\n

Meta Seamless Acceptable Use Policy<\/a><\/p>\n

Lays out Meta\u2019s rules for using Llama and other Meta AI models, including prohibitions on violence, child sexual exploitation content, harassment, hate speech, and disallowed data collection practices.<\/p>\n

Cohere Labs Acceptable Use Policy<\/a><\/p>\n

Defines Cohere\u2019s restrictions on content generation (e.g., plagiarism, hate, disinformation, defamation), requirements for data privacy<\/a>, and guidelines for content moderation in user applications.<\/p>\n

5. Updates to US State & Local Legislation \u2013 Deepfake Laws<\/h2>\n

Nebraska LB 383 \u2013 Prohibition of Generated Child Sexual Abuse Material <\/a><\/p>\n

Added in June 2025:<\/strong> Nebraska\u2019s new statute explicitly bans the creation, distribution, or possession of AI-generated child sexual abuse material (CSAM), aligning with federal legislation and other states\u2019 deepfake-related statutes. It imposes felony penalties and requires ISPs to remove flagged content within 48 hours.<\/p>\n

6. Changes in US State & Local Legislation \u2013 Privacy Laws<\/h2>\n

The June 2025 update harmonizes several privacy laws, but no net new statutes have been added beyond those included in March 2025. The key difference is that our privacy category\u2019s numbering and presentation have been updated; the same 16 state-level consumer privacy and AI-specific statutes (e.g., Utah AI Policy Act, Colorado SB 22-113) remain in scope.<\/p>\n

7. Newly Covered International Standards<\/h2>\n

OECD Framework for the Classification of AI Systems <\/a><\/p>\n

Added in June 2025:<\/strong> The OECD published a standardized taxonomy for AI systems, classifying them by modality (e.g., perception, reasoning, interaction), domain, and risk level\u2014supporting cross-border regulatory alignment and data sharing for incident reporting.<\/p>\n

OECD Common Reporting Framework for AI Incidents <\/a><\/p>\n

Added in June 2025:<\/strong> This framework establishes a minimum dataset and ontology for reporting AI-related incidents (e.g., bias events, security breaches, safety failures) to national authorities, helping policymakers track trends and coordinate response.<\/p>\n

CSET\u2019s AI Incidents Key Components for a Mandatory Reporting Regime <\/a><\/p>\n

Added in June 2025:<\/strong> The Center for Security and Emerging Technology (CSET) proposed a set of \u201ckey components\u201d (e.g., incident taxonomy, timelines, organizational context) that could underpin a legally mandated AI incident reporting regime\u2014informing U.S. congressional discussions.<\/p>\n

UNESCO\u2019s Ethical Impact Assessment <\/a><\/p>\n

Added in June 2025:<\/strong> UNESCO released an Ethical Impact Assessment (EIA) toolkit that guides organizations through a structured evaluation of social, cultural, and environmental impacts of AI deployments. It complements existing ethical AI principles by providing actionable steps for risk identification and stakeholder engagement.<\/p>\n

8. Summary of Operational Policy Additions<\/h2>\n

In addition to the expanded \u201cCovered Laws, Regulations, Frameworks & Standards,\u201d the June 2025 release introduces two brand-new policies. These are standalone documents in the suite (with dedicated Table of Contents entries) to address emergent needs in incident management and acceptable use:<\/p>\n

New: AI Incident Reporting Policy<\/h3>\n