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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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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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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

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Communicating Sustainable Development Goals in the Age of Artificial Intelligence

Muhammad Jameel Yusha'u

Palgrave Macmillan (2026), xlvii, 523 pp.

"This book is the first scholarly work dedicated to examining the complex relationship between artificial intelligence and communicating the UN Sustainable Development Goals. Moving beyond techno-optimist narratives, the book advances a critical theoretical intervention: the AI Sustainability Exclus ... more

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Advancing Open Source AI in India: Recommendations for Governments & Technology Developers

Aarushi Gupta, Anushka Jain, Urvashi Aneja, Shreeja Sen

Bonn; Delhi; Noida (IN): Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ); FAIR Forward – Artificial Intelligence for All; NASSCOM (2026), 80 pp.

"[...] this brief positions open source AI as a strategic policy option within India’s evolving AI ecosystem. It unpacks the critical importance of openness for India’s digital future, crystallising the key opportunities it offers to governments and developers alike. In doing so, it also explore ... 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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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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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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Reinventing Humanitarian Aid Procurement for the Age of AI

Giulio Coppi

Access Now (2026), 59 pp.

"Our research reveals that the organisational use of AI and algorithmic systems often happens through a process of unstructured integration rather than formal adoption. More precisely, cloud-based, algorithmic-enhanced functionalities and processes are making their way into humanitarian entitiesʼ t ... ech stack through updates and add-ons to preexisting products, slowly but inexorably seeping into most internal systems. A price-based procurement process designed around the static idea of digital asset purchase is inadequate to ensure adequate protection from hyperdynamic tech product development. This is especially evident looking at the growing power imbalances between private actors and nonprofits, and among nonprofits themselves. Digital technology is often heralded as key to democratise and equalise the playing field between humanitarian actors, by finally unlocking local actorsʼ access to resources traditionally reserved for international entities. But emerging trends seem to disprove such claims. The lack of adequate legal frameworks and safeguards, lagging investments in humanitarian funding for information and communication technology (ICT) resources and connectivity, and inadequate dedicated funding for digital programmes are thwarting these opportunities. More broadly, virtuous and responsible procurement practices are proving inadequate for protecting human rights alone, as tech companiesʼ commitments to human rights are replaced by defence-oriented pitches and the size of their human rights teams keeps shrinking. Preexisting digital divides are enhanced by several deep and subtle changes in traditional tech business models, resulting from the gradual cloudification of most common digital systems and the fast-paced, distributed nature of modern digital development. Even more worryingly, we are seeing new forms of digital divide well beyond the traditional gaps in digital capacity between local and small non-governmental organisations (NGOs) and international non-governmental organisations (INGOs) or UN entities. Cloud-based companiesʼ takeover of generic coordination platforms creates a constantly broadening chasm between the few organisations that are digitally mature, those who are barely retrofitting their processes to fit the new reality, and the broad majority forced to access the promised land of digital humanitarianism through the predatory gates of algorithmic and cloud servicesʼ free tech licensing. Our findings highlight how organisation-led digital development, building on open-source systems (despite some “openwashing” practices) and trusted external providers, remains the best option for risk reduction when it comes to algorithmic systems. However, in the short to medium term, this might not be financially and technically feasible for the majority of humanitarian actors, especially considering the catastrophic funding environment. To fill this gap, we propose a toolkit in an appendix to this report. This is a foundational governance model for transforming digital procurement from a transactional into a strategic business function, integrated across key organisational departments. By doing so, protection-mandated aid groups could move from running purchase-centred processes, to adopting a dynamic, proactive, and rights-based approach to tech management. This report also contains a set of recommendations for states and donors, the humanitarian community, tech companies, local aid actors and communities, research centres, and cyber experts." (Executive summary, pages 2-3) more

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Invisible Workers, Visible Harms: Perils and Precarities of AI Labour

Aapti Institute

Bonn: Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) (2026), 40 pp.

"Across South Asia, Southeast Asia, and Sub-Saharan Africa, data workers operate within fragmented and opaque supply chains characterised by informal employment arrangements, limited social protections, and a profound imbalance of power between workers, vendors, and the global technology firms that ... 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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How to Talk to AI (And How Not To)

Jamie Bartlett

London: Ebury Publishing (2026), 272 pp.

"[...] Most people still don't really understand how AI works, how to make the most of it – or what the dangers are. As some people use it to turbo-charge their productivity at work, others are falling into dangerous conspiracies, delusions and psychosis. In How to Talk to AI, award-winning techno ... 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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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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Justicia algorítmica en América Latina y el Caribe: Investigación, políticas públicas y ética de la inteligencia artificial

Consejo Latinoamericano de Ciencias Sociales (CLACSO); CIESPAL; UNESCO (2026), 105 pp.

"El volumen reúne los policy briefs elaborados en el marco de la convocatoria Justicia Algorítmica. Una inteligencia artificial ética para América Latina y el Caribe, lanzada conjuntamente por UNESCO Ecuador y el Consejo Latinoamericano de Ciencias Sociales (CLACSO), en el marco de una colaborac ... ión intensa, sostenida y productiva que ambas instituciones han consolidado en los últimos años. A ella se sumó El Centro Internacional de Estudios Superiores de Comunicación para América Latina, CIESPAL, responsable de la edición del libro en alianza con CLACSO y UNESCO, reafirmando la convicción de que la cooperación interinstitucional regional constituye un instrumento fundamental para pensar críticamente la tecnología, fortalecer la cultura democrática y promover políticas públicas capaces de atender a los grupos más vulnerables. Los trabajos seleccionados y aquí publicados son el resultado de un proceso riguroso, sistemático y situado: se recibieron 35 postulaciones, evaluadas en primera instancia por un Comité Internacional integrado por nueve especialistas provenientes de Ecuador, Argentina, Brasil y Francia. Posteriormente, UNESCO y CLACSO realizaron la revisión final atendiendo a la calidad técnica y científica, la coherencia metodológica, la consistencia del plan de trabajo y la pertinencia de los temas en relación con la Recomendación sobre la Ética de la IA y el equilibrio regional. El proceso concluyó con la selección de cinco propuestas, varias de ellas con participación de investigadoras e investigadores con trabajo en y sobre Ecuador. Los policy briefs que conforman este libro abordan problemas urgentes de la agenda regional sobre IA. Uno de los núcleos críticos es la transformación del trabajo y la desigualdad digital, un fenómeno particularmente agudo en América Latina y el Caribe, donde históricas brechas educativas, económicas y territoriales se ven amplificadas por una automatización que concentra oportunidades en sectores altamente especializados y desplaza tareas tradicionalmente realizadas por trabajadores menos calificados. La región enfrenta una doble asimetría: la del acceso desigual a tecnologías avanzadas y la de las capacidades necesarias para utilizarlas de modo significativo, reproduciendo patrones de exclusión que atraviesan género, etnia y condición socioeconómica. Otro foco central es la privacidad y la vigilancia algorítmica, ámbitos donde los riesgos son crecientes. La implementación de sistemas opacos —las conocidas “cajas negras” algorítmicas— que procesan datos personales a gran escala ha generado nuevas formas de discriminación, como el racismo algorítmico, sesgos de género, o modelos predictivos que penalizan a grupos históricamente vulnerados. Los casos de identificación errónea en sistemas de reconocimiento facial o la clasificación automatizada de supuestos “riesgos sociales” muestran que, sin marcos éticos y normativos sólidos, la IA puede reforzar desigualdades en vez de corregirlas." (Introducción, páginas 8-9) more

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Societies at an AI Crossroads: Choices and Value Conflicts

Ignas Kalpokas, Julija Kalpokienė

London; New York: Routledge (2026), 116 pp.

"As contemporary societies undergo profound transformations driven by artificial intelligence and related technologies, a unilinear vision of inevitable ‘progress’ and development increasingly dominates public discourse. This book offers a counterpoint, challenging such deterministic narratives ... more

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AI in the media: Kenya and South Africa

Karen Allen, Herman Wasserman, Nande Mbekela

Bonn; Stellenbosch (ZA): Deutsche Welle DW Akademie; Centre for Information Integrity in Africa (CINIA) (2026), 27 pp.

"This study shows that AI remains largely an underexplored, and often misunderstood, topic for journalism in South Africa and Kenya. Stories can be framed in overly positive terms, such as the potential AI holds for development and social change, or negative terms, by highlighting the extractive nat ... ure of big tech firms or the potential of AI to foster disinformation. Either way, these reports frequently lack a more nuanced understanding of the impacts of AI in African contexts. Fear of the impact of AI on the media workforce may also explain why reporters say they often face resistance by editors when seeking to get AI and tech stories commissioned. AI articles tend to be siloed and reactive, framed as business, security or technology stories. Seldom is AI presented in terms of its multidimensional, layered impact on society. Furthermore, the geopolitical aspect of AI rarely gets interrogated in mainstream media. Journalists interviewed for the study admitted that many of their stories were reactive, and identified areas where synergies could be harnessed from other parts of the world. On issues of AI and social media regulation, for example, where precedents have been set by countries such as Australia, South African and Kenyan media could use such precedents to develop locally focused stories. AI reporting often adopts a framing promoted by technology vendors who derive interest from presenting a one-dimensional, optimistic view of AI. The unintended consequences often go unreported unless supported by an investigation, such as the CCTV camera surveillance reportage by Swart referenced above. Furthermore, the topic is often presented through a Global North lens, with little contextualisation or interrogation of these issues from an African point of view amid resource and access constraints. This occurs against a background of under-developed policy frameworks for AI on the continent generally, and in newsrooms in particular. Consequently the playing field can be tilted in favour of AI and tech vendors who fill the vacuum with hyperbole and hype about what the technology is capable of delivering. At the same time, more nuanced and specialised reporting on AI is constrained by the familiar obstacles of journalism, such as lack of resources, over-stretched newsrooms and story competition. To enhance coverage of AI in Africa, a multifaceted strategy is needed. While training and AI literacy programmes can deepen the knowledge needed by African journalists to investigate the topic more comprehensively, increased investment in newsrooms can provide the structural support to provide more context-specific coverage of AI on the continent. Crucially, training and education of African journalists should be done from within the African context, as solutions cannot be imposed from newsroom contexts in the Global North." (Conclusions, page 21) more

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Critiquing Generative AI in Africa's Media Ecosystems

Lungile A. Tshuma, Trust Matsilele

London; New York: Routledge (2026), 215 pp.

"The book argues that as technology is a social construct reflecting existing power dynamics, those in the Global South, and in Africa in particular, are bound to be marginalised and have their knowledge, systems overlooked as the Global North dominates technological developments. The birth of AI ha ... more

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Navigating risks and rewards: How South African journalists use AI in the newsroom

Karen Allen, Herman Wasserman, Nande Mbekela

Johannesburg; Stellenbosch: Konrad-Adenauer-Stiftung (KAS), Media Programme Sub-Saharan Africa; Centre for Information Integrity in Africa (CINIA) (2026), 33 pp.

"This study demonstrates impressively how widely AI is already used in South African newsrooms – often, however, without systematic training, without editorial guidelines, and without tools adapted to African languages and contexts. The interviews make it clear that journalists stand between curio ... more

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Why Silicon Valley is turning to the Catholic Church: Priests and theologians want to shape the future of AI. Big Tech is listening

Elias Wachtel

The Atlantic, April 25 (2026)

"Silicon Valley and the Vatican may “seem like strange bedfellows”—but they each have something to gain from the other, @EliasWachtel argues. He examines why Big Tech is listening to priests and theologians who want to shape the future of AI." (Publisher description)