Recent Global Developments in AI Policy

"What is the Global Landscape of AI Regulation? Between new laws & revoked orders, the landscape of #AIRegulation is shifting quickly. Last week, as the US House passed a bill potentially banning all state AI laws for the next decade, there is an urgent need to clarify what "AI regulation" actually means & develop analytical tools that resist political shifts. We are excited to share that our paper, a joint collaboration between Stanford University and Harvard University researchers, introduces a taxonomy to capture the global landscape of AI regulation. With co-authors Shira Gur-Arieh, Tom Zick, PhD. & Kevin Klyman, we analyze emerging AI regulatory frameworks across five early movers–the EU, US, China, Canada, and Brazil– to identify patterns, divergences & blind spots. The taxonomy illustrates the breadth & depth of AI regulatory approaches by analyzing key metrics, including technology or application-focused rules, ex ante precautions or ex post liabilities, horizontal or sectoral regulatory coverage, maturity of the digital legal landscape, enforcement mechanisms & level of stakeholder participation. To democratize our findings, we collaborated with designers Vikramaditya Sharma, Steven Morse & Tanil Raif to translate dense legal texts into accessible outputs. Key takeaways: 1️⃣ We must clarify the term "AI regulation." The term is used ambiguously to describe both binding legal frameworks & voluntary industry guidelines. Lines are often strategically blurred between hard law (AI regulation) & soft law (AI policy). Such semantic ambiguity can mislead public expectations, create a false sense of protection & open the door to regulatory capture. 2️⃣ Innovation vs. regulation is a false dichotomy. China's experience shows it is possible to enforce mandatory safeguards while continuing to develop cutting-edge models like DeepSeek. While the intentions behind Chinese AI regulation differ from Western ones, for example to control political dissent, the coexistence of strict regulation & rapid innovation proves that the two are not mutually exclusive. Countries can lead the AI arms race while having legally-binding safety requirements. 3️⃣ Under the same umbrella term, not all AI regulations are equal. Some frameworks are more comprehensive than others. Hybrid AI regulations–combining both ex ante & ex post mechanisms and technology & application based rules–address societal harms and national security risks, while imposing obligations before and after deployment. 4️⃣ Civic engagement remains a blind spot. There is little data on whether civic consultations translate into meaningful, legal outcomes—or are merely performative." Good work from Sacha Alanoca (who wrote the above summary) and Berkman Klein Center for Internet & Society at Harvard University

The Financial Times has reported that Brussels is preparing a tougher 2026 enforcement push under the Digital Markets Act and Digital Services Act, with Google, Meta, Apple and X squarely in view. It also reported that the Trump administration is threatening retaliation, including tariffs and visa bans, over what it frames as European “censorship”. The DMA and DSA were built to curb platform dominance and to force accountability for illegal content and systemic risks. But enforcement now overlaps with AI in practice: recommendation systems, generative content, manipulative ad targeting, and algorithmic amplification. In fact the AI Offie in Europe is meant to take control of DSA AI enforcement under the proposed Digital Omnibus. If Brussels follows through, the effect will be to push global platforms towards EU-style governance controls even outside Europe. Washington’s response is the counter-model. Rather than argue over the substance of European laws, the Trump administration is threatening economic and diplomatic costs for applying them. The result is a new reality for boards and general counsel - AI compliance is now inseparable from geopolitical exposure. You may comply perfectly and still be caught in retaliation politics. While Europe and the US trade blows, China is quietly opening a different front. Beijing has released draft AI safety rules aimed at curbing suicide, self-harm and violence content, but with a telling addition: restrictions on “emotional manipulation”, including so-called “emotional traps” and false promises to users. The regulatory idea here is psychological safety by design. China is treating emotionally persuasive AI as a consumer harm category, akin to gambling or online addiction. That framing will not stay in China - Western regulators can reach similar outcomes through product safety, consumer law, youth protection and liability doctrines without passing a single “AI companion statute”. India is building another path. The Economic Times reported that the central government has asked states to submit proposals for AI Centres of Excellence under the IndiaAI Mission, explicitly aimed at strengthening AI capability and deployment. In Rajasthan, officials will unveil an AI-ML Policy 2026 next week, backed by a dedicated AI data centre in Jaipur. This is governance through capacity, procurement and infrastructure, not headline regulation. Three conclusions follow. First, the global AI rulebook is fragmenting into enforcement-first Europe, control-and-safety China, and capacity-and-deployment India. Second, AI regulation is increasingly a trade and foreign policy instrument, not merely a domestic consumer protection issue. Third, the next wave of obligations will be operational: disclosure, intervention protocols, logging, and systemic-risk mitigation that regulators can measure.

What to Read This Weekend – The EL PAcCTO 2.0 “Artificial Intelligence and Organised Crime” report (updated August 2025) offers one of the most comprehensive and operationally relevant examinations of AI’s dual role in law enforcement and organised crime to date. For compliance leaders, it is essential reading not just for its breadth of case studies but for the way it integrates regulatory, ethical, and strategic dimensions. The study illustrates how AI has moved far beyond automation into a transformative force for both legitimate and illicit networks. It details sophisticated criminal applications—AI-driven drones in drug trafficking, large-scale phishing tailored via language models, malware generation through unrestricted AI platforms, and deepfake-enabled fraud—while simultaneously mapping law enforcement responses, such as predictive analytics, automated licence plate recognition, and AI-assisted evidence analysis. Importantly, the document situates these developments within an evolving global governance architecture. It outlines binding instruments like the Council of Europe Framework Convention on AI and the EU Artificial Intelligence Regulation (REIA)—including their explicit provisions for high-risk law enforcement uses—and non-binding frameworks from the OECD and UNESCO that aim to safeguard human rights, transparency, and accountability. The gender and human rights sections should resonate with compliance functions overseeing ESG and ethics portfolios. They unpack the real risks of bias, discrimination, and exclusion embedded in AI systems, especially in contexts like facial recognition, recruitment algorithms, and digital violence, with an emphasis on the under-representation of women in AI policy development. This report offers actionable awareness in four critical areas: 1. Threat modelling – understanding AI-enabled criminal typologies and their operational signatures. 2. Regulatory alignment – anticipating how binding and voluntary frameworks will shape internal AI governance. 3. Ethics integration – embedding bias detection, transparency, and proportionality into technology deployment. 4. Cross-border cooperation – leveraging emerging EU–LAC digital alliances to build interoperable compliance capabilities. This is not just a policy paper—it is a tactical briefing for any compliance leader navigating AI risk across regulated sectors. #AI #FinancialCrimePrevention #Governance #RiskManagement #Regulatory #Compliance

So much happens so quickly in #AIgovernance that I’ve decided to launch a Month in Review. This will only spotlight the key developments that should be on your radar. With that, here’s my Top 10 for January: ▶️ The first International AI Safety Report was published. It synthesizes the state of scientific understanding of general-purpose AI, with a focus on managing its current and emerging risks. It’s a must-read filled with technical rigor, balanced policy perspectives, and tangible recommendations. 🔗 https://lnkd.in/e7vupCba ▶️ President Trump started the US down a new path by revoking the foundational 2023 executive order and directing his administration to develop an AI action plan within 180 days.  The National AI Advisory Committee promptly provided a 10-point framework. 🔗 https://lnkd.in/ehzErwiK (EO) 🔗 https://lnkd.in/exNjVb5y (NAIAC) ▶️ The US Copyright Office released a report on the copyrightability of AI-generated works, with nine conclusions or recommendations (and significant supporting research). 🔗 https://lnkd.in/eJhzRNfV ▶️ DeepSeek launched R1, captured attention, created confusion, and sparked concerns. And the global gyrations (and governance implications) are just beginning. 🔗 https://lnkd.in/eHNGQqtM ▶️ The EU AI Office unveiled a draft template that would require GPAI model providers to disclose a “sufficiently detailed summary” of the data used to train their models, including sources. 🔗 https://lnkd.in/e3rz8Zpi ▶️ California's Attorney General issued AI advisories informing consumers of their rights and companies of their obligations under existing law. This theme continues to resonate around the world, with many other regulators offering similar reminders. 🔗 https://lnkd.in/eFyazZDq ▶️ The US FTC finalized a settlement with IntelliVision over claims related to its facial recognition software. While not expressly tied to Operation AI Comply, the case serves as another example of how existing laws apply to AI and how regulatory enforcement will likely progress. 🔗 https://lnkd.in/efV3T5u6 ▶️ The Netherlands updated its AI impact assessment template, offering a new glimpse into the EU AI Act requirement. 🔗 https://lnkd.in/eURuYdKK ▶️ The US FDA proposed guidelines for AI-enabled medical devices and drug development. While not yet finalized, they signal support for innovation so long as rigorous scientific and regulatory standards are satisfied. 🔗 https://lnkd.in/e9eNVrXB (devices) 🔗 https://lnkd.in/epN64-6q (drugs) ▶️ The World Economic Forum released an “Industries in the Intelligent Age” Series, with detailed snapshots of AI’s applications and best practices across seven sectors. 🔗 https://lnkd.in/evRFN7ZB

I'm thrilled to announce the release of my latest article published by The Brookings Institution, co-authored with Sabrina Küspert, titled "Regulating General-Purpose AI: Areas of Convergence and Divergence across the EU and the US." 🔍 Key Highlights: EU's Proactive Approach to AI Regulation: -The EU AI Act introduces binding rules specifically for general-purpose AI models. -The creation of the European AI Office ensures centralized oversight and enforcement, aiming for transparency and systemic risk management across AI applications. -This comprehensive framework underscores the EU's commitment to fostering innovation while safeguarding public interests. US Executive Order 14110: A Paradigm Shift in AI Policy: -The Executive Order marks the most extensive AI governance strategy in the US, focusing on the safe, secure, and trustworthy development and use of AI. -By leveraging the Defense Production Act, it mandates reporting and adherence to strict guidelines for dual-use foundation models, addressing potential economic and security risks. -The establishment of the White House AI Council and NIST's AI Safety Institute represents a coordinated effort to unify AI governance across federal agencies. Towards Harmonized International AI Governance: -Our analysis reveals both convergence and divergence in the regulatory approaches of the EU and the US, highlighting areas of potential collaboration. -The G7 Code of Conduct on AI, a voluntary international framework, is viewed as a crucial step towards aligning AI policies globally, promoting shared standards and best practices. -Even when domestic regulatory approaches diverge, this collaborative effort underscores the importance of international cooperation in managing the rapid advancements in AI technology. 🔗 Read the Full Article Here: https://lnkd.in/g-jeGXvm #AI #AIGovernance #EUAIAct #USExecutiveOrder #AIRegulation

AI is not unregulated anymore. It’s becoming one of the most governed technologies in the world. And most businesses are not ready for it. Because AI is no longer experimental - it’s making real decisions in hiring, finance, healthcare, and security. Here’s what every business needs to understand 👇 Why AI regulation matters: Bias. Data misuse. Lack of accountability. These aren’t technical issues anymore - they’re legal and business risks. The global shift: Governments are moving fast with structured frameworks. Risk-based classification. Transparency requirements. Clear accountability. This is no longer optional. Key regulations shaping AI globally: - EU AI Act (Europe) Risk-based AI classification. High-risk systems require strict compliance. Some use cases are banned entirely. - GDPR (Europe) User consent. Data protection. Right to explanation. Privacy is now a design requirement. - NIST AI Framework (US) A practical approach to managing AI risks across the lifecycle. Helps companies operationalize governance early. - Executive Orders (US) Focus on safety testing, responsible deployment, and fairness in AI systems. Signals stricter laws ahead. - China AI Regulations Strict centralized control. Mandatory algorithm registration. Strong enforcement and compliance checks. - Singapore AI Model Flexible, business-friendly governance focused on transparency, explainability, and accountability. - OECD AI Principles Global baseline for AI policy - human-centered, fair, and accountable systems. - ISO/IEC Standards Standardizing AI practices globally - risk management, lifecycle governance, and reliability. - Algorithmic Accountability Laws Bias audits. Risk assessments. Documentation. Businesses must prove their AI is fair. - Global Data Protection Laws GDPR, CCPA, DPDP - data compliance is now core to AI systems. What businesses must do now: AI governance is no longer a technical add-on. It’s a core business function. → Build internal governance frameworks → Ensure transparency and accountability → Implement monitoring, audits, and documentation 💡 The big reality: AI is no longer unregulated innovation. It’s a regulated system with global oversight. The companies that win won’t be the fastest. They’ll be the most trusted. Because the future belongs to businesses that build compliant, responsible, and trustworthy AI systems.

One of the most important shifts in AI governance is happening quietly. Governments are starting to regulate compute, not just models. At first glance, this sounds highly technical. It is actually about power. A recent paper by Matteo Pistillo Suzanne Van Arsdale Lennart Heim and Christoph Winter on compute thresholds argues that training compute may become the key regulatory trigger for frontier AI systems - because compute is measurable, monitorable, and harder to hide than model capabilities themselves. If this approach scales, AI governance starts looking less like content moderation and more like financial supervision, and that has major implications. The strategic chokepoints are no longer only algorithms or applications. They are cloud infrastructure, semiconductor supply chains, chip access, training clusters, and verification systems. In other words: infrastructure becomes governance. The deeper implication is geopolitical. Only a small number of actors control advanced compute at scale. So the future of AI governance may depend less on whether governments can regulate "AI" in the abstract - and more on whether they can govern concentrated infrastructure ecosystems dominated by a handful of firms and jurisdictions. This also explains why AI sovereignty debates are intensifying globally. Because dependence on external compute infrastructure increasingly looks like strategic dependence itself. The next phase of AI governance may not primarily be about regulating intelligence. It may be about regulating access to the industrial base that produces it. #AIGovernance #ComputePolicy #DigitalSovereignty #TechPolicy #AIPolicy

AI governance often sounds abstract. But across the world, small bright spots are showing us what operational responsible AI can look like. → Singapore is building open-source AI testing tools to align compliance with practical validation, moving beyond checklists to scalable assurance. → Chile mandates public procurement of AI to require proof of fairness, bias testing, and data protection. Procurement is quietly becoming a lever for responsible AI markets. → Costa Rica is incubating feminist AI research to embed gender inclusion into design and policy, not as an afterthought. → Mexico is using machine learning to preserve endangered languages, reminding us that AI can strengthen, not erase, cultural diversity. → Croatia amended labor laws to regulate algorithmic management, ensuring workers have rights in the age of automated decision-making. These aren’t moonshots. They are practical interventions that operationalize responsible AI today. What connects these stories is their grounding in the Global Index on Responsible AI, a first-of-its-kind effort assessing 138 countries on their progress, gaps, and pathways toward rights-respecting, human-centered AI. Most countries have national AI strategies. Few translate them into enforceable, actionable protections for citizens, workers, or communities. The GIRAI report is a reminder: frameworks are not enough. Responsible AI requires measurable, enforceable action.

The global landscape of national #AI strategies (2024–2026) reveals a differentiated yet converging architecture of priorities, where nations are calibrating their approaches to align technological capabilities with sovereign, economic, and geopolitical imperatives. At the forefront are nations advancing compute- and infrastructure-centric doctrines, including the #US United States, #China, the #UK United Kingdom, #Japan, #SouthKorea, #Germany, and #France. These countries are engineering AI #leadership by deliberately building sovereign computational capacity, encompassing #semiconductor independence, hyperscale cloud #ecosystems, and high-performance #computing. Their strategic orientation reflects a recognition that control over computing constitutes the foundational layer of AI dominance, with direct implications for national #security, #defense systems, and industrial competitiveness in an increasingly algorithmic global #economy. In parallel, a distinct cohort is shaping the global discourse through #governance-first and #ethics-driven frameworks, notably #Canada, #Switzerland, #Finland, #Norway, and #Ireland. These nations are operationalizing trustworthy AI architectures, embedding principles of #transparency, #accountability, and #risk-based oversight into enforceable regulatory systems. Their approach positions #governance not as a constraint, but as a strategic instrument of influence, capable of setting international standards and anchoring global #trust in AI-enabled systems. Another group of nations is leveraging AI as a strategic lever for economic transformation and diversification, including the #UAE, the #UnitedArabEmirates, #SaudiArabia, #Singapore, #Qatar, and #Australia. These strategies are characterized by state-coordinated #investment, cross-sector integration, and global #talent orchestration, positioning AI as a catalyst for transitioning toward resilient, #innovation-driven economies. Here, #sovereignty is expressed through the capacity to embed AI across national value chains and smart #infrastructure. Simultaneously, a growing number of countries—including #India, #Brazil, #Egypt, #Malaysia, #Vietnam, #Chile, and #Colombia—are advancing adoption-centric and capacity-building strategies. Their focus is on scaling AI deployment across public services, strengthening #workforce capabilities, and fostering inclusive digital ecosystems. By prioritizing accessibility and practical application, these nations are positioning AI as a tool for societal advancement and accelerated development, often leapfrogging constraints of legacy infrastructure. Collectively, these strategic orientations underscore a transition toward multi-layered AI sovereignty, where compute, governance, economic transformation, and societal impact are increasingly interdependent pillars of national strategy.