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Artificial Intelligence for Older Adults: Concepts, Needs and Practices of Use

Diana Dutsyk

Kyiv: Ukrainian Media and Communication Institute (2026), 18 pp.

"To better understand the attitudes of older adults toward artificial intelligence, as well as to identify their needs in using it, we conducted focus group discussions, the results of which are presented in this analytical report. Overall, these discussions showed that there is often a stereotypica ... 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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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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Just Hiring! How to counter discrimination in AI-powered software for recruitment. A toolkit for software developers

M. Daviet, N. Baranowska, A. Mueller, C. Rus, C. Castillo, J. Saldivar, A. Gatzioura

FINDHR Fairness and Intersectional Non-Discrimination in Human Recommendation (2025), 86 pp.

"In this Toolkit, we offer introductory information and technical recommendations on how to mitigate the risk of discrimination in software development of systems used for recruitment. This Toolkit addresses software developers and product managers working or interested in this field, and it intends ... 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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L'uomo è un algoritmo? Il senso dell'umano e l'intelligenza artificiale

Paolo Benanti

Castelvecchi (2025), 60 pp.

"Il mito di Ulisse ci insegna che la ricerca umana di senso è guidata dall’intelligenza, nelle sue due declinazioni: νοῦς e μῆτις, intuizione e pratica. Dalla sinergia di queste facoltà sono nate le grandi invenzioni che segnano la nostra specie, a partire dalla «grande invenzione de ... more

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L'Afrique face à la corruption: La solution numérique pour les marchés publics

Roger Lokpatchu Bahemuka

Brice Arsène Mankou (préface)

Paris: L'Harmattan (2025), 132 pp.

"Ce livre met en lumière l'urgence d'agir face à l'un des fléaux les plus destructeurs du développement africain : la corruption dans les marchés publics. À travers une analyse rigoureuse et des solutions concrètes, Roger Lokpatchu Bahemuka explore comment les technologies - blockchain, intel ... more

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Measuring what matters: How to assess AI’s environmental impact

Geneva: ITU (2025), viii, 21 pp.

This report synthesizes key findings from a diverse range of sources, including academic literature, corporate sustainability initiatives, and emerging environmental tracking tools. Collectively, these documents provide a thorough overview of current methodologies for evaluating the environmental im ... more

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AI Literacy & Communication

Center for News, Technology & Innovation (CNTI), AI and Journalism Working Group (2025), 25 pp.

"This briefing focuses broadly on journalistic communication about AI. We first summarize research and frameworks that address the following questions about societal AI literacy: What does the public currently know about AI, and what does the public need to know about AI? We then move on to a synthe ... more

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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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Digital Progress and Trends Report 2025: Strengthening AI Foundations

Washington, DC: World Bank (2025), xix, 111 pp.

"The recent rapid evolution of artificial intelligence (AI) has outpaced society's ability to fully grasp its implications. Unlike technological shifts that have unfolded over decades, AI's integration is accelerating at an unprecedented speed and scale. Along with AI's immense opportunities come ne ... 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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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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Just Hiring! How to counter discrimination in AI-assisted recruiting within your organisation. A toolkit for HR professionals

M. Daviet, A. Mueller, N. Baranowska, C. Castillo, J. Saldivar, A. Gatzioura

FINDHR Fairness and Intersectional Non-Discrimination in Human Recommendation (2025), 73 pp.

"In this Toolkit, we offer introductory information and concrete recommendations on how to mitigate the risk of discrimination in algorithmic hiring systems within organisations. This Toolkit addresses HR professionals and recruiting agencies and intends to help them gain a better understanding of w ... more

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AI-Generated Disinformation in Europe and Africa: Use Cases, Solutions and Transnational Learning

Karen Allen, Christopher Nehring

Johannesburg: Konrad-Adenauer-Stiftung (KAS), Media Programme Sub-Sahara Africa (2025), xii, 158 pp.

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2025 Technology and Innovation Report: Inclusive Artificial Intelligence for Development

New York: United Nations Conference on Trade and Development (UNCTAD) (2025), xiii, 168 pp.

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Patterns of Inclusion: How Gender Matters for Automation, Artificial Intelligence and the Future of Work

Elisabeth Kelan

Oxford; 9781032669892 (pbk): Routledge (2025), xiv, 169 pp.

"It is widely presumed that digitalisation, automation and artificial intelligence (AI) shape the future of work. Yet gender is rarely considered in those debates. This path-breaking book, written by a leading thinker on gender, inclusion and organisations, Elisabeth Kelan, is based on in-depth rese ... more

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

CIPESA (2025), 74 pp.

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

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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

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

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