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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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AlgorithmWatch’s guidelines to use generative AI responsibly

Oliver Marsh, Michele Loi

Berlin: Algorithm Watch (2026)

"Whether you use ChatGPT, Claude or Gemini, Copilot or Perplexity – generative AI poses massive problems: many results are inaccurate and politically problematic, the systems’ energy and water consumption is enormous. At the same time, they have become an integral part of everyday life. Algorith ... 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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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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How-to note: Co-designing AI solutions with crisis-affected communities (beta version)

Sarah W. Spencer, Helen McElhinney, Michael Tjalve, Anjali Mazumder

CDAC Network; Alan Turing Institute; Humanitarian Advisory AI (2025), 17 pp.

"This how-to note offers practical guidance for humanitarian actors seeking to co-design AI solutions with crisis-affected communities. This is related to, though not intended to support the technical co-design of AI models, though there is a significant amount of research on this subject. This how- ... more

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Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task

Nataliya Kosmyna, Eugene Hauptmann, Ye Tong Yuan, Jessica Situ, Xian-Hao Liao, Ashly Vivian Beresnitzky, Pattie Iris: Maes Braunstein

Cambridge (MA): MIT Media Lab (2025), 206 pp.

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Künstliche Intelligenz im medialen Diskurs: Wird soziale Gerechtigkeit ausgeblendet?

Elke Grittmann, Lina Brink, Peter Kann

Frankfurt am Main: Otto Brenner Stiftung (2025), 84 pp.

[.] Auf Basis von über 2.000 Artikeln aus neun reichweitenstarken Print- und Onlinemedien untersuchen die Autor*innen, welche Themen in der Berichterstattung über KI gesetzt, wie Bezüge zu Fragen sozialer Gerechtigkeit hergestellt werden, welche narrativen Muster sich zeigen und welche Akteur*inn ... more

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Considerations and Practical Applications for Using Artificial Intelligence (AI) in Evaluations

D. Cekova, L. Corsetti, S Ferretti, S. Vaca

Rome: CGIAR Independent Advisory and Evaluation Service (IAES) (2025), 42 pp.

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Guía de implementación de protección de datos y uso responsable de inteligencia artifical en Bolivia

Estefani Cabrera Rojas, Nicoel Angel Sanchez Rojas

La Paz: InternetBolivia; Consorcio InDiCo Global (2025), 63 pp.

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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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United Nations Activities on Artificial Intelligence (AI)

Geneva: ITU (2025), xvi, 500 pp.

"If 2023 was the year of generative artificial intelligence, 2024 was marked by the rapid expansion and adoption of AI, driving waves of innovation across nearly every conceivable domain. The United Nations system has kept an encouraging pace, redoubling efforts to harness the power and potential of ... more

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Künstliche Intelligenz in politischen Kampagnen: Akzeptanz, Wahrnehmung und Wirkung

Simon Kruschinski, Pablo Jost, Hannah Fecher, Tobias Scherer

Frankfurt am Main: Otto Brenner Stiftung (2025), 108 pp.

"Im ersten Teil geben wir einen Überblick über den Einsatz von generativer KI in der politischen Kampagnenkommunikation. Insbesondere die generativen KI-Tools ChatGPT und Midjourney werden zunehmend in politischen Kampagnen eingesetzt, um menschenähnliche Texte beziehungsweise fotorealistische Bi ... more

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Guidelines for the mindful and ethical use of AI

Aachen: Catholic Media Council (CAMECO) (2025), 2 pp.

Understand AI and its Tools -- Understand Algorithms -- Maintain a Critical Perspective -- Protect Your Data—And Others' Too -- Stay Informed about AI Ethics and Policy -- Use AI Thoughtfully—Not for Everything

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Inteligencia Artificial: Guía de uso para informativos en radio comunitaria
Key Guides

Tito Ballesteros López

Red de Emisoras Comunitarias del Magdalena Medio (AREDMAG) (2025), 24 pp.

"La guía que aquí presentamos propone caminos prácticos para incorporar la IA en los noticieros radiales, desde la redacción automatizada hasta la interacción con los oyentes en tiempo real, pasando por la generación de contenidos sonoros, el monitoreo de fuentes y el análisis de impacto. Cad ... more

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The Routledge Handbook of Artificial Intelligence and Philanthropy
Deep Insights

Milos Maricic, Giuseppe Ugazio (eds.)

London; New York: Routledge (2025), xxv, 57o pp.

"The Routledge Handbook of Artificial Intelligence and Philanthropy acts as a catalyst for the dialogue between two ecosystems with much to gain from collaboration: artificial intelligence (AI) and philanthropy. Bringing together leading academics, AI specialists, and philanthropy professionals, it ... more

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Thinking with AI: Machine Learning the Humanities

Hannes Bajohr (ed.)

London: Open Humanities Press (2025), 269 pp.

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AI for Good Innovate for Impact. Interim Report 2025

Geneva: ITU (2025), xxxiv, 828 pp.

"Artificial intelligence (AI) is reshaping the way we address complex societal challenges, offering new possibilities in areas such as healthcare, climate resilience, education, and digital inclusion. The Innovate for Impact project was launched in 2024 to identify, support, and showcase practical A ... more

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Generative AI & Journalism: Content, Journalistic Perceptions, and Audience Experiences

T. J. Thomson, Ryan J. Thomas, Michelle Riedlinger, Phoebe Matich

Melbourne: RMIT University (2025), 39 pp.

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Rethinking Open Source in the Age of Foundational AI Models
Deep Insights

Greg Maly

ICTworks, July 29 (2025)

"As the global artificial intelligence community wrestles with the tension between scale and accessibility, recent work by researchers at Lelapa AI offers a compelling example of what inclusive, efficient AI can look like. Around the world, the push to shrink massive language models into deployable, ... more

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How Do You Explain ChatGPT to a Community Health Worker?

Wayan Vota

ICTworks, December 2 (2025)

"I’ve been diving deep into groundbreaking research on community health worker (CHW) perceptions of AI applications in rural India. The truth is more complex than the rosy predictions flooding our sector about artificial intelligence transforming global health. The findings expose a troubling gap ... more

Inteligencia Artificial: Guía de uso para informativos en radio comunitaria Key Guides

The Routledge Handbook of Artificial Intelligence and Philanthropy Deep Insights

Rethinking Open Source in the Age of Foundational AI Models Deep Insights

Inteligencia Artificial: Guía de uso para informativos en radio comunitaria
Key Guides

The Routledge Handbook of Artificial Intelligence and Philanthropy
Deep Insights

Rethinking Open Source in the Age of Foundational AI Models
Deep Insights