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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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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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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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The Protocol Gap: Brazil

Sérgio Spagnuolo, Ester Borges, Bruno Fiaschetti, Paula Miraglia

Journalism Relay Project; Momentum; International Fund for Public Interest Media (IFPIM) (2026), 12 pp.

"An overwhelming majority of news websites in Brazil have either no AI-scraping policy at all or a very permissive approach. As of November 2025, only 7.2% of Brazilian news websites disallow at least one AI crawler through the use of a robots.txt file, even though most news sites (75%) have a robot ... 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 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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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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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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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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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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Thinking with AI: Machine Learning the Humanities

Hannes Bajohr (ed.)

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

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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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AI With Purpose: How Foundations and Nonprofits are Thinking About and Using Artificial Intelligence

Elisha Smith Arrillaga, Seara Grundhoefer, Christina Im

Center for Effective Philanthropy (CEP) (2025), 39 pp.

"While AI is widely used by nonprofits and foundations alike — most often for internal productivity and communications purposes — leaders share common concerns about AI in their work. They also agree that foundations do not currently have a strong grasp of nonprofits’ AI-related needs. Perhaps ... 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.

" Guided by the CGIAR-wide Evaluation Framework, integrating AI tools requires a governance approach to balance innovation with ethical responsibility, ensuring transparency, fairness, accountability, and inclusivity. This Technical Note encourages and guides CGIAR evaluators to ethically explore, n ... egotiate, and experiment with AI tools: Explore: Evaluators are invited to discover how AI, especially GenAI, can enhance evaluation efficiency, from scoping and data analysis to reporting. The Note provides practical guidance on AI applications and examples to support creative yet responsible exploration. Negotiate: Integrating AI should be openly discussed with commissioners, stakeholders, and teams. The Note prioritizes jointly defining boundaries, expectations, and ethical parameters (transparency, accountability, and data sensitivity) at each phase of evaluation. Use AI Responsibly: While AI tools are evolving, evaluators are encouraged to pilot and iterate their use. The document supports experimentation through practical tips, prompt examples, and tool selection criteria, all while emphasizing documentation and learning from each use case. Effective AI governance is grounded in core principles: Transparency requires clear documentation of AI tool usage, data sources, model limitations, and decision-making processes. Accountability involves assigning responsibility for AI decisions and outputs and establishing oversight and redress mechanisms. Fairness and inclusion must proactively mitigate bias and discrimination, with particular attention to underrepresented groups and data gaps. Data privacy and security must align with applicable data protection regulations and ensure secure handling practices. Human oversight ensures that evaluators retain control over processes and can intervene as needed. In operationalizing ethical AI governance in CGIAR evaluations, due diligence is required in assessing AI tools for ethical alignment before deployment: reviewing the transparency of vendors, the documentation of models, and their intended use cases. Where relevant, components involving AI—especially those engaging human subjects or sensitive data—should undergo ethics review. AI applications must be adapted to the local and cultural contexts in which evaluations are conducted, as what is suitable in one setting may be inappropriate in another. Additionally, participants should be informed about the use of AI systems and the implications of data collection or processing to ensure informed consent. Ethical AI governance should be embedded in the entire evaluation lifecycle. During the design phase, evaluators should define AI tools to use, why they are selected, and assess risks. In data collection, AI tools should be used in ways that uphold data privacy and protection standards and avoid reinforcing harmful stereotypes or excluding groups. During the analysis phase, the role of AI in supporting interpretation should be documented, with an acknowledgment of limitations or biases. In dissemination, documentation, and reporting, AI’s contribution, limitations, and human validation should be disclosed. By rapid adaptation of content across formats, languages, and complexity levels, AI opens possibilities for broader, more inclusive communication of findings. Finally, the follow-up phases should include a reflection on the ethical implications observed and how these lessons can improve future evaluations." (Executive summary) more

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Practical Machine Learning: A Beginner's Guide with Ethical Insights

Ally S. Nyamawe, Mohamedi M. Mjahidi, Noe Elisa Nnko, Salim A. Diwani, Godbless G. Minja, Kulwa Malyango

Boca Raton: CRC Press; Chapman & Hall (2025), xii, 214 pp.

"The book provides an accessible, comprehensive introduction for beginners to machine learning, equipping them with the fundamental skills and techniques essential for this field. It enables beginners to construct practical, real-world solutions powered by machine learning across diverse application ... more

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Zwischen Wertschätzung und Widerstand: Algorithmische Kompetenz junger Menschen am Beispiel der Kurzvideoplattform TikTok

Leonie Alatassi, Sascha Hölig, Philipp Kessling

Hamburg: Hans-Bredow-Institut (2025), 64 pp.

"In der vorliegenden Studie wurde untersucht, was Jugendliche und junge Erwachsene über die Funktionsweise des algorithmischen Empfehlungssystems (AES) von TikTok wissen, inwiefern sie damit interagieren und welche Emotionen dabei involviert sind. Dazu wurden sechs Fokusgruppen (n=31) in drei deuts ... chen Großstädten mit jungen Menschen im Alter zwischen 16 und 24 Jahren sowie Follow-up-Einzelinterviews (n=12) durchgeführt. Es zeigt sich ein übergreifend hohes Bewusstsein für algorithmische Empfehlung bei TikTok als erlebter Zustand („knowing“), der sich aus der eigenen Nutzungserfahrung ergibt. Es gibt altersbezogene Unterschiede beim Wissen um Einflussfaktoren und insgesamt ein begrenztes Wissen zur Datensammlung und -verarbeitung sowie damit einhergehend geringe Bedenken und Reflexionen über die Preisgabe personenbezogener Daten. Such-, Video-, Profil- und Sozialinteraktionen sind in die alltägliche Nutzung der Plattform eingebettet und finden größtenteils unbewusst, ohne explizite Steuerungsabsicht, statt. Die Teilnehmenden können innerhalb der von der Plattform vorgegebenen Strukturen agieren und Handlungsziele durchsetzen, erleben die (technischen) Grenzen jedoch als kaum beeinflussbar. Häufig fehlen Wissen und Motivation, um tiefergehende Einstellungen, etwa zum Datenschutz, der personalisierten Suche oder Profileinstellungen, aktiv zu verändern. Emotionen spielen in der Interaktion mit der For-You-Page (FYP) eine große Rolle. Die Teilnehmenden erleben positive Gefühle der Wertschätzung, indem der Algorithmus als personalisierter Begleiter erlebt wird, der individuelle Vorlieben erkennt, inspiriert und das Nutzungserlebnis angenehm gestaltet. Gleichzeitig entstehen negative Emotionen wie Genervtheit, Unbehagen oder Kontrollverlust, insbesondere bei unpassenden Inhalten, einer wahrgenommenen Übersteuerung durch den Algorithmus oder der eigenen Nutzungsdauer. Diese Gefühle führen teils zu Widerstand in Form von aktiven Interaktionshandlungen oder zu selbstregulativen Strategien (z. B. Löschen der App) und sind Ausdruck eines Autonomiestrebens. Die Unterscheidung zwischen passiven Kontakten mit politischen Informationen auf TikTok (push: „ich schaue mir politische Videos an, die mir gezeigt werden“) und aktiven Informationshandlungen durch Kuratierungspraktiken wie das Liken von Videos, das Folgen und Nachschauen von Profilen oder die Nutzung der Suchfunktion (pull: „ich nutze die Plattform, um mich zu informieren“) ist essenziell. Grundsätzlich empfinden es die meisten Teilnehmenden als praktisch, wenn Nachrichten oder politische Themen in Form von kurzen Videos auf der FYP angezeigt werden, weil das passive Scannen dieser Inhalte das Gefühl erzeuge „auf dem aktuellsten Stand“ zu sein. Die wenigsten Teilnehmenden konsumieren gezielt politische Inhalte, indem sie nach Themen suchen, politische Videos favorisieren oder politische Profile anschauen und diesen folgen. Die Beschäftigung mit Nachrichten und Politik im Kontext der TikTok-Nutzung widerspricht dem Eskapismus- und Unterhaltungsbedürfnis vieler Teilnehmender. Es gibt eine Grundskepsis gegenüber TikTok, die sich zum einen auf das Image der Plattform, „nicht vertrauenswürdig“ zu sein, stützt, und zum anderen auf die Angst vor „falschen Informationen“ und die damit zusammenhängende Wahrnehmung, dass es sich bei TikTok nicht um eine „seriöse“ Quelle, sondern eine reine Unterhaltungsplattform handle." (Überblick über die wichtigsten Ergebnisse, Seite 5) 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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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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La inteligencia artificial: Una mirada pastoral desde América Latina y el Caribe

José Sols Lucía (coord.)

Bogotá: Consejo Episcopal Latinoamericano y Caribeño (CELAM) (2025), 97 pp.

"El trabajo incluye desde nociones generales sobre Inteligencia Artificial hasta propuestas prácticas a los obispos del continente. El sumario incluye antecedentes históricos y evolución de la IA, aspectos éticos, antropológicos y teológicos, recoge el magisterio de la Iglesia, asume el desaf� ... more

The Routledge Handbook of Artificial Intelligence and Philanthropy Deep Insights

The Routledge Handbook of Artificial Intelligence and Philanthropy
Deep Insights