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Real Rights in the Virtual World: Human Rights in the Age of Artificial Intelligence and Virtual Reality

Riku Neuvonen, Jukka Viljanen (eds.)

Cheltenham, UK: Edward Elgar Publishing (2026), xi, 245 pp.

"Riku Neuvonen and Jukka Viljanen examine the next developmental phase of information networks, positioning human rights at the centre of their analysis. Chapters assess the current problems facing the digital world as well as new challenges posed by virtual worlds, including access to information a ... more

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Automated Disinformation and Malicious AI Swarms: Risks for Democracy and Development in Africa

Daniel Thilo Schroeder, Philipp M. Lutscher, Samba Dialimpa Badji, Stefan Brenner, Wenchao Dong, Tsehaye Haidemariam, Saheed Bidemi Ibrahim, Amanuel Tesfaye Kebede, Jonas R. Kunst, Johannes Langguth, Pauline Lemaire, Mulatu Alemayehu Moges, Lukasz Olejnik, Kristin Skare Orgeret, Gerald Walulya

Online: arXiv, First (2026), 26 pp.

"Generative artificial intelligence is reshaping how information is produced, accessed, and circulated, while enabling disinformation campaigns of increasing scale and sophistication. There is currently no clear evidence that fully autonomous AI swarms conduct influence operations at scale, but thei ... more

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AI Adoption in the Global South

Emmanuel Bahindi, Antonio Kisembo, Echeru Rodney Oluma

Kampala: Media Challenge Initiative (2026), 14 pp.

"This paper examines how artificial intelligence is being adopted, and resisted, in the newsrooms and journalism schools of the Global South, with a close focus on East Africa." (About this paper, page 2)

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AI Companions Reduce Loneliness

Julian De Freitas, Zeliha Oğuz-Uğuralp, Ahmet Kaan Uğuralp, Stefano Puntoni

Journal of Consumer Research, volume 52, issue 6 (2026), pp. 1126–1148

"Chatbots are now able to engage in sophisticated conversations with consumers in the domain of relationships, providing a potential coping solution to widescale societal loneliness. Behavioral research provides little insight into whether these applications (apps) are effective at alleviating lonel ... more

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Uso crítico y ético de la Inteligencia Artificial en la educación
Deep Insights

Santiago García Gago, Inés Binder

São Paulo: Campaña Latinoamericana por el Derecho a la Educación (CLADE); Educación en Voz Alta (2026), 150 pp.

"Esta guía sistematiza y resume los contenidos del curso virtual “Uso crítico y ético de la Inteligencia Artificial en la educación”, celebrado en tre los meses de octubre y diciembre de 2025 [...] El curso se planificó como una introducción conceptual a los distintos ámbitos que abarca ... la Inteligencia Artificial, entendida como un sistema sociotécnico complejo y multidimensional. La idea es que estos conceptos apoyen a docentes y educadores en el diseño de estrategias que incorporen la IA crítica y éticamente en los procesos educativos. Por lo tanto, no es manual técnico de aplicaciones ni tampoco uno de estrategias pedagógicas. Se concibió como un compendio de fundamentos teórico-prácticos que sustenten el diseño de las estrategias para incluir aplicaciones de IA en las aulas. Para ello, en el capítulo primero, se desmitifica el desarrollo tecnológico desde la sociología y la economía política preguntándonos quiénes son los dueños de la IA y qué intereses económicos y geopolíticos dirigen su destino. En el capítulo dos se abordan los conceptos técnicos, las definiciones y tipologías que permiten abrir la caja negra de la Inteligencia Artificial y entender cómo funciona. También se enumeran algunas aplicaciones, tanto las privativas como modelos open source que permiten experimentar en el ámbito educativo o usar con mayores niveles de privacidad estas herramientas. El capítulo tres, alejándose de la lógica instrumental que analiza las tecnologías desde los usos “buenos” y “malos”, problematiza las relaciones de poder que configuran la IA y los desafíos a los que nos enfrenta clasificados en siete dimensiones: técnicas, éticas, ecológicas, de género, políticas, laborales y de seguridad y privacidad. Y cierra con una breve exposición de los esfuerzos legislativos, tanto a nivel internacional como regional y nacional, para regular la Inteligencia Artificial. En el capítulo cuarto, se exponen diversos aspectos cognitivos y del El capítulo quinto, complementando el anterior, aborda estrategias concretas para la inclusión ética de la IA en los procesos de aprendizaje desde tres abordajes: con pedagogías centradas en la persona, integrales e interdisciplinares y basado en la tecnología y no en la aplicación." (Introducción, páginas 9-11) more

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SAFE AI: A Governance Framework for Humanitarians using AI. How to turn SAFE AI principles into practical action

Helen McElhinney, Anjali Mazumder, Michael Tjalve, Suzy Madigan, Sarah Spencer

CDAC Network; Alan Turing Institute; Humanitarian AI Advisory (2026), 93 pp.

"The SAFE AI Framework is designed to support humanitarian organisations on their AI journey, and navigate the risks raised. Organisations procuring AI systems or designing AI projects will benefit from each tool within this framework, which is intended as a practical approach to using, designing, p ... 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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How Russia Is Reshaping Command and Control for AI-Enabled Warfare

Kateryna Bondar

Washington, DC: Center for Strategic and International Studies (CSIS) (2026), 20 pp.

"This paper examines how Russia is transforming its command and control (C2) architecture under wartime pressure, how these changes shape the country’s incremental move toward battlefield-required software solutions, and what lessons U.S. policymakers can learn from Russia’s experiences. Focusin ... g on both strategic ambitions and battlefield practice, the takeaways below summarize how automated C2 systems, unmanned platform management software, and emerging AI applications are being developed, adapted, and scaled within Russia’s military ecosystem. 1. Russia is no longer prioritizing the construction of a single, comprehensive automated C2 architecture comparable to Western joint concepts; instead, it is reallocating effort toward tactical, task-specific software, driven by battlefield necessity. Prolonged, high-intensity combat in Ukraine exposed the limits of centralized, system-wide C2 modernization and elevated the importance of accelerating the tactical kill chain. The emergence of systems such as the “Svod” Tactical Situational Awareness Complex and other integrated reconnaissance-strike tools reflects a pragmatic shift in which operational control of unmanned systems and real-time battlefield management now deliver greater military value than achieving end-to-end C2 integration. 2. Because unmanned systems now conduct up to 80 percent of Russian fire missions, the center of gravity in C2 innovation has shifted toward software that manages drones and integrates them with artillery and other fire units. Civilian engineers and volunteer developers have focused on closing this gap by building tools that provide situational awareness, automate fire correction, and link unmanned aircraft systems (UAS) operators directly to firing units. Russia’s “Glaz/Groza” software complex demonstrates this trend, functioning as a unified reconnaissance-strike workflow that converts drone footage into targeting data and compresses the time from detection to impact from hours to minutes. 3. The Russian military assesses its AI capabilities for visual and audio data processing as relatively mature, placing computer vision, sensor fusion, and signal analysis at technology readiness level (TRL) 6–9, while natural-language processing remains at an early, experimental stage, TRL 1–3. This disparity reflects a deliberate prioritization of AI applications that deliver immediate battlefield utility—such as target recognition, guidance, and autonomous terminal functions for unmanned systems—and where abundant data and combat validation are available. By contrast, text analysis AI, which underpins document processing and higher-level C2 decision support, remains constrained by immature architectures, limited certified software, and organizational barriers, slowing progress toward fully AI-enabled command workflows. 4. Within Russian C2 systems, AI is primarily envisioned as a support function rather than a replacement for human decisionmaking. Russian military doctrine assigns AI two core roles: enhancing the processing and interpretation of sensor data and providing predictive decision support through forecasting, scenario generation, and recommendations for commanders. Across strategic and tactical levels, AI is intended to augment situational awareness and analytical capacity, while formal authority and responsibility for decisions remain firmly with human commanders. 5. Russia began its C2 digitalization effort by building a dense layer of standards governing terminology, system architecture, hardware-software integration, and information management. This standardization drive, coupled with the transition to the domestically controlled Astra Linux operating system, reflects an attempt to create a unified technical foundation capable of supporting data integration, interoperability, and future AI insertion across the command hierarchy. While this framework provides structural coherence, it has not, on its own, resolved deeper institutional and methodological constraints that continue to limit system-wide C2 integration. 6. To enable AI-driven tactical software, the Russian military launched a systematic data collection effort in 2025 focused on unmanned operations and strike outcomes. The emerging data infrastructure aggregates UAS video feeds, operator telemetry, strike effects, and individual pilot performance metrics, each linked to unique personal identifiers. These datasets serve multiple functions simultaneously: operational analysis, training evaluation, and the creation of labeled data for AI model development, establishing a feedback loop that ties battlefield performance directly to software refinement. 7. Despite efforts to reduce dependence on foreign commercial technologies, Russia’s military AI development remains heavily reliant on open-weight models and civilian software ecosystems. The transition from tools such as AlpineQuest and Discord toward domestic alternatives like ZOV Maps and Astra-based platforms reflects a push for security and sovereignty at the application layer. At the same time, Russian developers actively adapt open-weight and commercially available AI models, including Mistral, Qwen, LLaMA, YOLO, and related architectures, for military use, embedding them into on-premise, tightly controlled environments. This hybrid approach allows Russia to mitigate sanctions and accelerate AI adoption without building foundational models from scratch." (Executive summary) more

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The Hype Literacy Toolkit for Journalists

Jascha Bareis, Andreu Belsunces Gonçalves, Johannes Klingebiel, Steffen Leidel

Online: DW Akademie (2026)

"This course introduces the study of hype as an urgent force that shapes technological development, economics, politics and journalism. It is suited for people working in journalism and media but also serves as an introduction to hype for anybody interested, such as students, designers, academics or ... more

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The People-Centered AI Playbook. Playbook Capsule

Dalberg Data Insights (2026), 44 pp.

"This people-centered playbook [...] is designed to give you the essentials of AI adoption in a quick, accessible format, with core concepts, foundational steps, and key frameworks without overwhelming detail. Built for impact-driven teams in low-resource or non-tech-first contexts, it answers the m ... more

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AI Ethics, Bias & Governance in Media

Emmanuel Bahindi, Antonio Kisembo, Echeru Rodney Oluma

Kampala: Media Challenge Initiative (2026), 22 pp.

"This paper sets out how newsrooms and journalism schools can use artificial intelligence ethically: how bias enters AI systems and compounds through the news pipeline, why the dominant ethics frameworks are largely Western and what an African, Ubuntu-grounded alternative offers, and how a newsroom ... more

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Hallo, wie fühlst du dich heute? Wie künstliche Intelligenz unsere Beziehungen verändert

Christoph Koch

Zürich: Kein & Aber (2026), 272 pp.

"Christoph Koch hat ein Jahr lang mit Menschen gesprochen, die mit KI leben — mit denen, die sich verlieben, denen, die trauern, denen, die Chatbots als Therapie nutzen. Er hat mit KI-Forscherinnen und Psychologen geredet. Mit Anwälten und Geschädigten. Und mit den Maschinen selbst. Herausgekomm ... 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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Generative AI and democracy: Study on advantages and risks of the use of generative artificial intelligence in public debate and democratic processes

Natali Helberger, Claes de Vreese, Gionata Bouche, Agustin Ferrari Braun, Max van Drunen, Simon Kruschinski, Nicolas Mattis, Sophie Morosoli, Laurens Naudts, Charis Papaevangelou, Theresa Seipp, Fabio Votta, Teresa Weikmann

Strasbourg: Council of Europe, Steering Committee on Democracy (CDDM) (2026), 77 pp.

"The key finding of this report is that it is not enough to make sure that the technology is safe and no risk to human rights and public values. GenAI’s impact and potential for the realisation of fundamental rights depends on the societal, organisation and regulatory context in which it operates, ... the possibility for institutions to make value-driven choices and regulators that actively promote public innovation where the interests of society as a whole outweigh purely economic interests of a concentrated handful of technology companies. This report has made the argument that GenAI and its implications for human rights and democracy must be seen in the context of the configurations of infrastructural and political power, and who is able and entitled to decide the course and character of innovation. [...] The report makes a number of recommendations to the Council of Europe: 1. Provide substantive guidance and standardisation: As a human rights organisation, the Council of Europe is particularly well prepared to develop more substantive guidance and identify, concretise and monitor risks to democracy, human rights and political equality, be that in the way GenAI is used in democratic practices like elections, the more structural implications for democratic institutions (such as the free press) as well as the democratic governance of AI. Next to a focus on risks, the Council of Europe can also usefully develop guidance how AI can advance democracy and human rights and develop a value-driven interpretation of “public interest innovation” to inform States, companies and standardisation processes. 2. Incentivise and enable public interest innovation: Next to the risked-based approach to AI that is also enshrined in the Council of Europe’s Framework Convention, the Council of Europe could develop guidance on policies and governance approaches that enable and incentivise public interest innovation. It could help identify the democratic and human rights implications of procurement choices, and develop narratives to prioritize value-oriented innovation, open-source datasets, decentralisation and interoperability above the promotion of scale and innovation driven by a concentrated number of technology companies. 3. Strengthen democratic resilience and ability to govern: The Council of Europe, and the CDDEM specifically, could have an important role in exploring mechanisms and regulatory solutions to map and remedy existing dependencies and identify mission-critical elements of a European digital ecosystem, for example through monitoring the use of GenAI in elections, stimulating the development of public interest information infrastructures and strengthening the ability of states to develop democratic and inclusive ways of governing GenAI. Next to digital autonomy, empowering public accountability actors like civil society, equality bodies, academics or investigative reporters to participate in the governance of the digital transformation are critical conditions for strengthening democratic resilience. Finally, democratic resilience also means enabling more meaningful citizen engagement, including of otherwise often marginalised groups, for example through transparency, literacy and meaningful participation in decision making." (Executive summary) more

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Curricula and Curriculum Development for AI Journalism

Emmanuel Bahindi, Antonio Kisembo, Echeru Rodney Oluma

Kampala: AI Media Lab, Media Challenge Initiative (2026), 31 pp.

"This paper is a practical manual for building AI journalism curricula. It sets out the competency frameworks, from UNESCO to the African Criteria, that define what a curriculum must achieve; it presents the design methods of backward design and constructive alignment; it shows how to write measurab ... more

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AI-Assisted Journalism and Media: Opportunities and Challenges
Deep Insights

Pham Chien Editors: Thang, Aruchamy Prasanth

Cham, Switzerland: Springer, 1st (2026), 172 pp.

"This edited volume examines how artificial intelligence is transforming journalism and newsroom practices, balancing practical opportunities with professional and ethical challenges. Its chapters cover AI-supported news gathering, verification, automated content production, audience engagement, new ... 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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New AI Literacy Framework helps schools prepare learners for the age of artificial intelligence

Corporate Author: European Commission

Brussels, Belgium: European Commission (2026)

"This resource introduces the EU-OECD AI Literacy Framework for primary and secondary education, designed to help schools integrate AI literacy systematically into teaching and learning. It goes beyond learning how to operate AI tools by emphasizing critical understanding, responsible use, ethical i ... more

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Taking a Look at the most common AI myths & debunking them the Superrr way!

Berlin: SUPERRR (2026), 22 pp.

"How do we want to use technology, and when do we hit the brakes? As part of our work in 2026, we held 14 public-facing “AI mythbusting” sessions at libraries across Berlin, Germany. We were astonished by the strong public response and clear need for judgement-free spaces to talk about AI. Colle ... more

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AI is here. Are we ready?

Eva-Maria Verfürth et al.

D+C: Development and Cooperation, issue 4 (2026), pp. 31-58

"AI is already having a profound impact on our daily lives. The key question is who controls the technology. It is not only in conflicts that AI accelerates the spread of deceptively realistic fakes and makes it harder to distinguish reliable information from manipulation. At the same time, it opens ... more

Uso crítico y ético de la Inteligencia Artificial en la educación Deep Insights

AI-Assisted Journalism and Media: Opportunities and Challenges Deep Insights

Uso crítico y ético de la Inteligencia Artificial en la educación
Deep Insights

AI-Assisted Journalism and Media: Opportunities and Challenges
Deep Insights