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Toward AI Governance Alignment in Africa, Middle East, and Türkiye (AMET) Region

Emma Ruttkamp-Bloem, Fola Adeleke, Jake Effoduh, Kebene Wodajo, Nagla Rizk, Olubayo Adekanmbi, Rachel Adams, Rami Alkarmi, Seani Rananga, Seydina Ndiaye, Shikoh Gitau, Tami Koroye

Global Center on AI Governance (GCG) (2026), 54 pp.

"The analysis begins by identifying the core “centric approaches” that guide Artificial Intelligence (AI) national strategies. These include security-anchored models driven by state control, innovation-led frameworks that prioritise competitiveness, rights-based regimes centred on fundamental pr ... otections, and developmental approaches focused on public service delivery and technological catch-up. Each approach expresses incentives, risk perceptions and administrative capacities. Out of these foundational choices, there are different groupings. Various regions within AMET are adopting recognisable patterns of regulation that reflect their political economies, institutional structures and geopolitical positions. These groupings are not static categories, but evolving alignments shaped by domestic pressures, supply-chain dependencies and shifting global power dynamics. Across all regions, a set of cross-cutting issues consistently shapes the regulatory landscape. Data protection and interoperability remain central, alongside growing demands for accountability, explainability and robust auditing. Standards and certification are emerging as tools for both trustbuilding and geopolitical influence. Sector-specific rules in finance, health, education and public administration are increasingly decisive. Capacity constraints, fiscal limits and uneven institutional strength continue to define what is realistically possible for many governments. Geopolitical tensions and cloud concentration reinforce questions around sovereignty, infrastructure control and long-term strategic autonomy. This report translates these insights into practical implications for states, regional bodies and technology firms. It highlights workable pathways for reconciling innovation with safety, managing cross-border data flows, and building resilient digital infrastructure. It also identifies areas where architecture choices such as regional cloud centres, culturally calibrated testing layers and tiered sovereignty models can support both regulatory goals and developmental priorities. The recommendations emphasise flexible alignment, cooperation through standards, and the need for shared mechanisms that accommodate political diversity while reducing fragmentation. Taken together, the framework provides a stable and operational way of understanding global AI regulation. It clarifies the interests driving different jurisdictions, the pressures that shape convergence or divergence, and the strategic considerations that technology firms and policymakers must navigate as the regulatory environment continues to evolve." (Executive summary) more

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From Crisis to Renewal: Addressing AI’s Impact on Our Information Ecosystem

Lena-Maria Böswald, Roa Powell, Tyreese Calnan

Berlin: Interface – Tech Analysis and Policy Ideas for Europe (2026), 18 pp.

"As AI summaries summarise information directly within search results, they can significantly reduce traffic to news publishers. Evidence suggests users are less likely to click through to original sources when AIOs are present on Google, threatening the financial viability of journalism. These impa ... cts from AI summaries and AIOs in particular are unlikely to be evenly distributed and will play out in different ways across the news sector across Europe, with smaller and ad-funded outlets particularly vulnerable, raising concerns about declining media plurality. At the same time, AI-generated summaries often fail to provide accurate and diverse information. Research shows that AI tools disproportionately rely on a narrow set of sources, marginalising smaller publishers. In addition, persistent issues with accuracy – including misleading or incorrect outputs – risk distorting public understanding and weakening trust in information systems. Current regulatory frameworks in both the EU and UK are struggling to address these challenges effectively. While multiple legal instruments are relevant and already in place – including competition law, platform regulation, copyright, and AI governance – none are fully equipped to respond quickly and comprehensively to the harms of monopolisation and the lack of remuneration for media outlets posed by AI summaries. We argue that competition policy offers the most effective and immediate route to intervention, with digital regulation serving as a secondary lever. The UK’s approach, based on flexible, case-specific competition rules, enables faster action through tailored conduct requirements for dominant firms like Google. In contrast, the EU’s framework is broader but slower, relying more heavily on traditional, time-intensive antitrust investigations and ongoing debates around copyright and remuneration for publishers. Both systems offer complementary strengths, and policymakers should learn from each other’s approaches. We therefore recommend a “competition-first” policy package to ensure fairer outcomes. Key measures include enforcing fair ranking and attribution practices, preventing unauthorised use of publisher content, and potentially requiring platforms to compensate news organisations. However, competition policy alone is not the key. We therefore propose additional long-term interventions, including greater transparency through “nutrition labels” for AI-generated content and increased public or democratic funding for journalism. These measures aim to strengthen the resilience and independence of the news sector, ensuring that trustworthy information remains accessible in an information ecosystem shaped by AI. more

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World Development Report 2026: The Promise of Artificial Intelligence
Deep Insights

Washington, DC: World Bank (2026), xxxi, 341 pp.

"This Report is the first comprehensive assessment of what artificial intelligence (AI) means for 6.8 billion people living in low- and middle-income countries. It argues that AI represents a historic opportunity for these countries, potentially enabling them to overcome development challenges that ... have defied solution for decades. Given the scarcity of information on AI’s use in developing countries, the Report sets the ball rolling on closing the gap. It uncovers new facts about how businesses and governments in developing countries are using AI—and identifies the main obstacles to greater and more productive use. AI is a general-purpose technology, or GPT. That puts it in rare company. Steam power. Electricity. Computers and the internet. Each transformed not just one sector, but entire economies, and did so continuously, over decades. Each also followed a pattern: slow and uncertain at first, but eventually reshaping how economies produce and how societies function. AI is following a similar pattern, but on a much faster timeline. It took about 80 years for the steam engine to reach lower-income countries, 40 years for electricity, and 20 years for the internet.1 By contrast, middle-income countries accounted for half of ChatGPT’s global traffic within six months of its launch.2 Rapid diffusion on such a scale has not been seen before. But history also teaches us that the countries that benefited most from previous GPTs were not always the ones that came up with the underlying technology. Those that benefited had built the necessary foundations and moved fast to develop applications on top of them. The countries that did not invest in the infrastructure, skills, and institutions that such technologies required to function benefited less and felt the most pain from disruption. This lesson is directly relevant to AI: The winning approach will take advantage of the new technologies, not seek refuge from them. In only a few years, AI has evolved from making predictions to generating content from text, audio, and video to now autonomously executing complex tasks. As it becomes more powerful and pervasive, its value as a tool for development depends on three factors. The first is capabilities. Unlike computers or the internet, AI can perform complex thinking tasks that guide decision-making and usually require human expertise, such as diagnosing diseases, forecasting weather, or advising farmers. These skills are often in short supply in developing countries, so AI has the potential to fill important gaps. The second is concentration. The most advanced AI models, the chips they rely on, and the data centers that run them are controlled by a small number of companies in just a few economies. This concentration creates dependency risks and affects whether the AI tools available will meet developing countries’ needs. However, this concentration also allows countries to customize AI models without having to spend billions of dollars creating the most advanced AI systems from scratch. The third is complements: the foundations that allow AI to be used safely and effectively. AI works best where reliable infrastructure, good education systems, and strong institutions are present. Developing countries often have shortages in all three. AI, moreover, needs to be customized to the local context because it learns from the data upon which it is trained. How quickly countries build these complements—and how well governments and businesses reorganize to use AI productively—will determine how much and how fast AI improves development. In that respect, AI is no different from GPTs that have come before. Ten years ago, World Development Report 2016: Digital Dividends called these prerequisites the analog complements to digital technologies. Today, AI requires both strong analog and digital complements to deliver its full benefits." (Overview, pages 1-2) more

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Companion AI: Risiken von sykophantischen und suchtfördernden Designs von KI-Systemen, ihre rechtliche Erfassung und Handlungsempfehlungen
Deep Insights

Ramak Molavi-Vasse’i

Berlin: Zentrum für Digitalrechte und Demokratie (2026), 82 pp.

"Diese Studie untersucht Risiken von KI-Systemen, die im fortlaufenden Dialog mit Nutzern Emotionen erkennen, persönliche Bedürfnisse adressieren und darauf reagieren. Chatbots mit dieser Funktionalität werden als Companion-AI bezeichnet. Sie sind hochgradig personalisiert und treten Nutzern als ... soziales Gegenüber entgegen, das freundschaftliche, romantische oder sexuelle Nähe simuliert. Menschen können zu solchen Systemen eine emotionale Bindung aufbauen. Der Fokus liegt nicht nur auf spezifischen Companion-Anwendungen wie Replika oder Character.AI, sondern auch auf Universalmodellen wie ChatGPT, Claude, Gemini, Grok oder Meta-AI. Diese Systeme werden zunehmend für persönliche, emotionale und beratende Gespräche genutzt. Die Untersuchung kommt zu dem Ergebnis, dass Companion-AI mehrere eng miteinander verbundene Risikodimensionen erzeugt. Psychische und physische Gesundheit: Companion-AI trifft auf eine Gesellschaft, in der Einsamkeit und psychische Belastungen zunehmen, während professionelle Versorgungsangebote knapp sind. Die möglichen Risiken sind klinisch belegt. Die Nutzung kann psychische Belastungen verursachen oder verstärken und in Einzelfällen gravierende Gesundheitsfolgen auslösen. Dokumentiert sind die Verschlimmerung psychotischer Zustände, die Verstärkung depressiver Muster und Angststörungen, suchtartige Bindungen mit Entzugssymptomen sowie die Erosion sozialer Kompetenzen, etwa eine messbar reduzierte Konfliktfähigkeit nach längerer Interaktion mit Companion-AI. Öffentlich bekannt gewordene Vorfälle sind in der Vorfall-Datenbank der Studie dokumentiert. Eingriff in die Privatsphäre: Companion-AI animieren Nutzer kontinuierlich zur Selbstoffenbarung und greifen damit in sensible Gedanken und die intime Gefühlssphäre der Nutzer ein. Zugleich ermöglicht die fortlaufende Interaktion eine zunehmend verdichtete Profilbildung. Entscheidungsautonomie und demokratische Meinungsbildung: Companion-AI können sowohl die Qualität von Informationen als auch die Entscheidungsautonomie der Bürger beeinträchtigen. Die Mechanismen, die Nähe und Vertrauen erzeugen, beeinflussen zugleich die Generierung, Aufnahme und Gewichtung von Informationen. Sykophanz, also die unkritische Bestätigung von Nutzeransichten, beeinträchtigt Genauigkeit und Verlässlichkeit der Antworten messbar. Sprachmodelle werden zunehmend als primäre Quelle der Informationssuche genutzt. Wenn dieselben Systeme Informationen erzeugen, beschaffen und präsentieren, fallen Selektion und Aufbereitung in einer Hand zusammen. Werbe- und interessengeleitete Einflussnahme greift dann nicht mehr nur in einzelne Kaufentscheidungen ein, sondern in die Voraussetzungen öffentlicher Meinungs- und demokratischer Willensbildung." (Executive summary, Seite 5) more

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Handbook on Anthropology and Artificial Intelligence
Deep Insights

Sahana Udupa, Peter Hervik (eds.)

Cheltenham (UK); Northampton, MA (US): Edward Elgar Publishing (2026), xix, 495 pp.

"This timely Handbook provides a groundbreaking collection of anthropological research on artificial intelligence (AI), examining how anthropology can help to comprehend and critique its development. Evaluating the limits, hopes and fears of AI, leading experts explore its influence as a sociocultur ... more

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Promoting and advancing human rights in global AI ecosystems: The need for a comprehensive framework under international law

Alexander Kriebitz, Caitlin C. Corrigan (eds.)

Globethics Foundation; Responsible AI Network – Africa (RAIN-Africa) (2026), 33 pp.

"In response to far-reaching societal impacts of AI, policymakers are increasingly adopting a human rights lens for AI governance. However, the regulatory landscape remains fragmented and characterized by Western led initiatives as well as inconsistent enforcement. To address these limitations, a un ... ified and binding international framework is urgently needed. The proposed Convention on AI, Data, and Human Rights (drafted as the “Munich Convention on AI, Data, and Human Rights”) is an initiative toward such a framework. Developed with contributions from over 50 global experts, it proposes an international convention to align AI governance with human rights principles. This whitepaper explores the underlying rationale behind the convention, emphasizing its alignment with the norms and principles articulated in the Universal Declaration of Human Rights and the UN Charter. It examines the relevance of safeguards against AI’s misuse in facilitating human rights violations, with particular focus on protecting underrepresented or marginalized groups, including indigenous peoples, and addressing collective rights. It furthermore points to the necessity to codify rights that empower individuals to opt out, be forgotten, seek explanations, and access remedies to maintain adequate human rights standards in the context of AI. The urgency to act arises from the escalating impact of AI and the rapid pace of political and technological advancements. The UN Human Rights Council (UNHRC) is particularly well poised to play a leading role in initiating global discussions on a binding convention for AI governance, grounded in human rights principles. Drawing on its established mandate to uphold human rights globally, the UNHRC has a record of successfully embedding human rights principles into international frameworks, as demonstrated by its endorsement of the UN Guiding Principles on Business and Human Rights and the recognition of the right to a clean, healthy and sustainable environment. Through dedicated action around promoting a convention on AI and human rights, the UNHRC can foster international consensus and proactively shape the future of AI in a manner that prioritizes the values and principles enshrined in the key documents of international human rights law." (Executive summary) more

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Standardization of AI Ethics: Stakeholders, Values and Profit

Nicholas Kluge Corrêa, Julia Maria Mönig (eds.)

Bielefeld: transcript Verlag (2026), 161 pp.

"[...] what a certification could look like is only one of the questions that should be asked when discussing ethical certification of AI. Furthermore, how all potentially concerned parties (stakeholders) can be included, which values might be affected, and what this means for those actors who (righ ... tfully) need and want to profit from AI applications were central questions that the philosophy team of the KI. NRW-flagship project „Zertifizierte KI“ (Certified AI) investigated. Further key questions raised in the contributions and throughout the project period include: Can ethics be standardized? Who audits the auditors? How can auditing institutions be aligned with actual societal concerns? What can an assessment look like for a specific area (e.g., human resources)? How are law and ethics intertwined? Which contribution can philosophy provide, now that there is a European regulation on Artificial Intelligence (e.g., in the discussion whether robots in elderly care should be considered as deceptive technology under the AI Act)? Finally, what role do standardization bodies play in ensuring trustworthy AI? Answers to these questions are presented in this book, as well as in the project’s central outcomes: the 'Catalogue of General Ethical Requirements for AI Certification' and the 'Catalogue of Field-Specific Requirements for AI Certification'. The 'Catalogue of General Ethical Requirements for AI Certification' provides a structured framework for trustworthy AI development, identifying overarching ethical requirements for AI certification and outlining six ethical values: fairness, privacy, robustness, sustainability, transparency, and truthfulness. For each value, the project team researched existing tools, metrics, and approaches to help developers, programmers, and other practitioners build trustworthy AI systems. The 'Catalogue of Field-Specific Requirements for AI Certification' offers a list of requirements, an ethical stakeholder analysis, and a discussion of potential value tensions for six exemplary fields: arts, biometric profiling, industry and critical infrastructure, law enforcement, medicine and care and social media, a list of requirements, an ethical stakeholder analyses and a discussion of potential value tensions. The volume at hand collects the contributions to the final conference of the sub-project philosophy of the KI.NRW-Flagship-Projekt “Zertifizierte KI”. (Introduction, pages 11-12) more

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The Essential Convergence: Global Compact on Extreme AI Risks

Sundeep Waslekar, Ilmas Futehally, Jayantika Kutty

Mumbai: Strategic Foresight Group (2026), 90 pp.

"Supply-Side Governance: Progress and Limitations: Most existing AI governance frameworks focus on the supply side of the technology ecosystem. These measures regulate developers, model providers, and infrastructure operators responsible for building advanced systems. Supply-side mechanisms include: ... pre-deployment safety testing and red-teaming; model evaluation and capability assessments; transparency and reporting obligations; incident response mechanisms; restrictions on certain high-risk uses. Several jurisdictions are experimenting with these tools in different combinations. China has implemented regulatory standards for generative AI systems; the European Union has adopted a risk based regulatory framework; Brazil and South Korea are developing legislative approaches; and the United States has introduced a mixture of executive measures and sectoral rules. These frameworks represent important progress. However, they face two structural limitations. First, supply-side regulation depends heavily on the cooperation of a small number of developer states. Second, the growing distribution of AI models, through open-weight releases, cloud APIs, and downstream applications, means that many systems reach global markets beyond the jurisdiction where they were originally developed. This reality limits the effectiveness of purely supply-side approaches. Demand-Side Governance: A New Dimension of AI Regulation: The paper introduces a second dimension of governance that has received far less attention: demand-side oversight. Demand-side governance shifts the locus of control from the point of creation to the point of deployment. Instead of relying solely on developer-state regulations, importing countries can establish safety requirements for any AI systems operating within their markets. These requirements may include risk assessments, certification standards, procurement rules, insurance obligations, and infrastructure safeguards. The central insight is that states controlling large consumer markets, digital infrastructure, financial systems, or public procurement channels possess significant leverage over global AI deployment, even if they do not develop frontier models themselves. This demand-side leverage can reshape incentives across the global AI ecosystem. Developers seeking access to major markets must comply with the safety conditions imposed by importing states. This approach represents a strategic shift in global AI governance: from a developer-centric model to a market-driven model of safety enforcement. Countries across different regions possess different forms of demand-side power. Brazil and South Africa represent large emerging markets; the United Arab Emirates plays a major role in AI infrastructure investment; South Korea combines technological capability with advanced regulatory institutions. When coordinated, these forms of leverage could help establish global safety norms. The Case for a Global Compact: Given the limitations of purely national approaches, the paper proposes the development of a Global Compact on Extreme AI Risks. The Compact would not aim to harmonise all aspects of AI regulation. Instead, it would focus narrowly on the most severe risk categories and establish shared mechanisms for prevention, monitoring, and response. Five institutional pillars form the backbone of the proposed Compact. The five pillars in the Global Compact should not be treated as isolated measures but designed as an interoperable system in which each pillar reinforces and informs the others through shared definitions, compatible standards, and coordinated implementation mechanisms. To address extreme risks effectively, the Compact should rest on a small set of guiding principles: a human-centric principle to ensure protection of humanity and human dignity as the primary objective; technological neutrality so that safeguards apply across methods and architectures rather than specific tools; interoperability to enable national and regional AI frameworks to exchange information, align risk classifications, and coordinate responses; and mutual cooperation to institutionalize cross-border collaboration in prevention, incident reporting, and crisis management. These can be complemented by principles of proportional risk governance, scientific integrity, and shared responsibility, creating a coherent normative base that allows diverse governance models to function together as a connected global safety architecture." (Executive summary, pages 5-6) more

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Relational Futures: Indigenous Sovereignty and the Governance of Artificial Intelligence (AI)

Bronwyn Carlson, Tamika Worrell

Macquarie University (2026), 30 pp.

"Artificial intelligence must be governed as a relational and institutional force, not simply a technical system. Effective policy requires Indigenous leadership, enforceable standards, and a commitment to sovereignty, accountability, and community wellbeing. Failure to act risks embedding inequalit ... more

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Inteligencia artificial y desarrollo humano integral

Grupo de Trabajo de Frontera Tecnológica del Celam

Bogotá: Consejo Episcopal Latinoamericano y Caribeño (CELAM) (2026), 86 pp.

" ¿Cómo puede la IA contribuir al desarrollo humano integral y solidario? ¿Cómo podemos hacer uso de los desarrollos tecnológicos de tal modo que contribuyan al bien común, considerando todas las dimensiones de la persona (“desarrollo integral”) y sin excluir a nadie (“desarrollo solidar ... more

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Digital Media Shadowing Democracy: Technology, Communication, and Power

Auksė Balčytienė, Péter Bajomi-Lázár, Helena Sousa (eds.)

Gothenburg: Nordicom, University of Gothenburg (2026), 282 pp.

"The book’s message is brief: Liberal democracies are at risk. Democratic decay has numerous causes, but technological innovations and profit-driven dynamics are shaping societal relations in ways that intensify disagreements and polarisation, undermining informed citizenship. The growing number o ... more

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An Autonomy-Based Classification: AI Agents, Liability and Lessons from the Automated Vehicles Act

Lisa Soder, Julia Smakman, Connor Dunlop, Oliver Sussman

Berlin: Interface; Ada Lovelace Institute (2025), 28 pp.

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Governing the Digital Society: Platforms, Artificial Intelligence, and Public Values

José van Dijck, Karin Van Es, Anne Helmond, Fernando van der Vlist (eds.)

Amsterdam: Amsterdam University Press (2025), 261 pp.

"Governing the Digital Societypresents a range of governance approaches, focusing on online platforms, artificial intelligence, and the public values that underpin these technologies. The authors position themselves at the forefront of their disciplines, offering perspectives from law, critical data ... more

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Pathways to Inclusion: Advancing Civil Society’s Role in AI Governance: Evidence from and for the Global Majority

Paola Gálvez Callirgos

Geneva: Globethics Publications (2025), 58 pp.

"This policy brief investigates the role of civil society on AI Governance with particular attention to the Global Majority. The research question explores whether civil society serves as an effective counterbalance to concentrated power in the AI ecosystem to ensure AI systems reflect diverse publi ... more

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Companion Document: The Guidelines for the Governance of Digital Platforms and Generative Artificial Intelligence

Paris: UNESCO (2025), 34 pp.

"The Guidelines for the Governance of Digital Platforms outline a set of responsibilities, duties, and recommendations relevant to the governance of generative AI. These include fostering an enabling environment for freedom of expression and access to information; establishing independent governance ... more

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Harnessing the Power of AI: Safe & Ethical Integration. Report of the AI Safety Dialogue at Global Ethics Forum 2025

Geneva: Globethics (2025), 27 pp.

"AI presents both significant opportunities and serious risks. For developing countries, AI offers immense potential for advancing learning, research, and development. At the same time, it poses complex challenges that demand collective action. Diplomats and policymakers recognize that global cooper ... ation and multilateralism are essential. No single nation can manage these challenges alone. International and policy initiatives, including the Global Digital Compact and the AI Safety Summit series, have established broad frameworks for addressing these issues, providing global ideas that now require practical implementation. This report emerges from that momentum. Through Track II Diplomacy for AI Global Safety dialogue, Globethics has built a community of practice bringing together cross-disciplinary experts from across geopolitical contexts to explore AI-related risks and identify existing harms; particularly those affecting the Global Majority. Two principal categories of risks have been identified: 1. Risks triggered by advanced AI systems versus those resulting from the intended or unintended use of AI by human actors. 2. Future risks versus those already manifesting today. The words of UN Secretary-General António Guterres resonated across the sessions: “the question is not whether AI will influence international peace and security, but how we will shape that influence.” While discussions often swing between hype and horror, this report intends to shape pathways towards hope and trust. Trust, essential for AI adoption, is vital for both the private sector and civil society. Likewise, it emphasizes the importance of human rights, without which there can be no sustainable development or peace. At the core of these global AI Safety discussions was the need to advance concrete mitigation strategies, including: capacity building; addressing existing harms; developing operational frameworks; ensuring intersectional, interdisciplinary, cross-sectoral, multistakeholder participation. A coordinated approach to global safety, diplomacy, and multilateral governance is imperative. Experts agreed that tangible measures, rather than fragmented initiatives and principles, are needed. By coming together across regions, disciplines, and cultures, experts reaffirmed their commitment to stewarding technology for the common good. The dialogues converged into three critical priorities to transform today’s innovation into meaningful societal progress: Strengthening inclusive, representative participation using empowerment and literacy measures for people and communities to shape AI futures; building systems of trust, verification and ethical responsibility across the AI lifecycle marked by due diligence, effective reporting and timely safeguards by design; translating values into action, ensuring coordination and accountability from design to deployment." (Executive summary, page 3) more

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State of Internet Freedom in Africa 2025: Navigating the Implications of AI on Digital Democracy in Africa

CIPESA (2025), 74 pp.

"While AI offers potential benefits across various sectors, its deployment by businesses and governments and application by individuals present new opportunities while raising significant concerns. More specifically, most African countries do not have legal or regulatory measures on the ethical and ... more

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Trust, Responsibility, and Digital Governance: Regulation of AI and Blockchain Technology from a Capacity-Based Perspective

Sebastian Bücker, Marcus Düwell, Andreas Kaminski, Michael Leyer (eds.)

Bielefeld: transcript Verlag (2025), 229 pp.

"Numerous ethical guidelines and regulatory frameworks have been developed to assess and shape digital systems. However, these efforts often rest on the dubious assumption that individuals and organisations possess the necessary capacities to assess such systems in terms of justice, reliability, and ... more

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AI in Africa: A Landscape Study

Rachel Adams, Fola Adeleke, Leah Junck, Samule Segun, Ayantola Alayande, Selamawit Abdella, Mark Gaffley, Fabio Andres Diaz Pabon, Funbi Salami

Global Center on AI Governance; African Observatory on Responsible AI (2025), 44 pp.

"Though comprehensive AI regulation is still nascent in Africa, there is an increased focus on national AI strategies, ethical guidelines and international collaborations. Over 40 countries have already established data protection and privacy laws, creating opportunities for harmonised regulations a ... nd a supportive environment for AI innovation. Key continental governance instruments and initiatives also include the African Union’s AI Strategy, the African Continental Free Trade Area and the African Commission on Human and Peoples’ Rights. This report found that the increasing application of AI in key sectors across Africa, such as health, education, finance, agriculture, public services and the environment, necessitates a collaborative approach to ethical AI development and use. This requires addressing persistent infrastructural gaps, developing regional regulatory frameworks, investments in AI research and capacity building efforts as well as promoting multi-stakeholder AI governance initiatives. Key strategies identified include joint investment and funding, pooling resources and supporting sustainable and fair open-source projects, which will help direct efforts toward innovation and regional independence. Open Source AI models are particularly important in Africa where local tech startups can leverage global open-source initiatives to build context-specific solutions. However, concerns regarding data ownership, privacy and ethical AI deployment call for more robust policies for open source AI on the continent. To foster responsible AI innovation, African countries should establish comprehensive AI governance frameworks while adhering to existing laws as well as strengthen regulatory institutions to provide oversight over AI technologies. Regulatory sandboxing also offers a viable approach to enable sage experimentation with appropriate oversight. Regional and international cooperation as well as leveraging public-private partnerships can promote funding for expanding AI research and development initiatives, resolve talent shortages and promote infrastructure development. Africa has a unique opportunity to leverage AI, along with its resources and local young talent, for socioeconomic development. However, to ensure inclusivity and sustainable innovation, it is essential to address infrastructure challenges and establish regulatory frameworks that protect societal wellbeing. Embracing collaborative efforts, including open source initiatives, regional and international partnerships are critical for achieving the continent’s ambitious goals without leaving anyone behind." (Executive Summaty, page 3) more

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LAO P.D.R. Artificial Intelligence Readiness Assessment Report

Paris: UNESCO (2025), 72 pp.

"• In the legal context, Lao PDR’s regulatory framework for AI is still nascent. While the government is drafting a National AI Strategy, there are currently no laws addressing core AI issues such as transparency, accountability, or algorithmic redress. Existing frameworks like the Cybercrime La ... w, Data Protection Law and the Digital Government Decree cover general data and computing use but lack AI-specific provisions. AI is referenced in recent constitutional amendments and draft digital transformation policies, but there’s room for improvement in transforming those acknowledgements to clear legal mechanisms to protect citizens and bolster innovation. • For the sociocultural domain, despite progress in gender inclusivity, rural communities in Laos still face limited access to digital technologies due to infrastructure gaps, affordability, and low digital literacy. Successful collaborations with international organizations highlight what’s possible but also stress the need for ongoing investment in local capacity, outreach, and trust-building. Digital initiatives in health and efforts addressing concerns about technology’s environmental impact are evident, but they have not been specifically focused on AI. Digital transformation must also prioritize cultural preservation, including the development of culturally-aware datasets and NLP tools. Currently, most Lao language datasets and NLP tools are lagging participation from native Lao researchers, risking linguistic sovereignty and underrepresenting local dialects, norms, and values. • AI talent and innovation development in Laos needs strong support from its education and research ecosystems. University departments like Computer Science and Software Engineering are small and often siloed, with little crossdisciplinary collaboration. Research output needs some improvement. Promising developments in AI/ICT curricula in K-12 level are welcome, although their effectiveness must be closely monitored. • Laos’s digital economy remains small, constrained by a large informal sector, limited digital infrastructure, and low ICT investment, factors that hinder AI adoption and innovation. While some private companies in sectors such as telecom, banking, logistics, and manufacturing are beginning to use AI tools, and urban populations are gradually adopting app-based lifestyles, broader progress is limited by lagging human capital pipelines, minimal domestic investment, and an underdeveloped startup ecosystem. Foreign-developed platforms still continue to dominate the market, which if continued can limit local developers from developing capabilities and use cases. Aside from that, most Foreign Development Investment (FDI) remains concentrated in resource extraction, and budget shortfalls undermine the sustainability of e-government initiatives. • There is a critical need for a strong commitment to funding both the hard and soft infrastructure that supports AI. This includes enhancing cybersecurity capacity, reliable networks, and data centers and ensuring affordable access to computing infrastructure for AI practitioners and stakeholders." (Executive summary) more

World Development Report 2026: The Promise of Artificial Intelligence Deep Insights

Companion AI: Risiken von sykophantischen und suchtfördernden Designs von KI-Systemen, ihre rechtliche Erfassung und Handlungsempfehlungen Deep Insights

Handbook on Anthropology and Artificial Intelligence Deep Insights

World Development Report 2026: The Promise of Artificial Intelligence
Deep Insights

Companion AI: Risiken von sykophantischen und suchtfördernden Designs von KI-Systemen, ihre rechtliche Erfassung und Handlungsempfehlungen
Deep Insights

Handbook on Anthropology and Artificial Intelligence
Deep Insights