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„Hi, how may I assist you?” Chatbots for Better Service Delivery. Practical Guide, Applications and Principles in Development Cooperation
Key Guides

Daniel Brumund, Prashant Pratap Singh, Lisa Werkmeister

Bonn; Eschborn: Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) (2026), 48 pp.

"The guide provides lessons-learnt for building AI chatbots and explores: motivations and requirements for developing a chatbot; functional types of AI chatbots suitable for different projects; content and knowledge base behind chatbots; technological architectures for AI chatbots; steering and man ... more

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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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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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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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European AI Competitiveness Beyond Frontier Models: Insights From AI Startups in Five Strategic Markets

Nicole Lemke

Berlin: Interface – Tech Analysis and Policy Ideas for Europe (2026), 37 pp.

"AI development is frequently described as a race for frontier models. But considerable economic value will also be generated by AI development and adoption in existing markets. This data brief examines where EU competitiveness stands in the five applied markets where most of the EU’s AI startup a ... ctivity in our dataset takes place – horizontal enterprise software, healthcare and life sciences, manufacturing and supply chain, finance and insurance, and research and development tools. Drawing on data from Dealroom.co covering 24,468 active AI startups launched between 2012 and 2025 across the EU, Switzerland, the UK, and the U.S., we compare geographic patterns at the level of regions, countries, and cities through original analysis [...] Manufacturing and supply chain is the only market where the EU accounts for a larger share of AI startups than the U.S., though our data likely underestimates the number of U.S. startups in general. This suggests the competitiveness gap across applied AI markets may be even larger than our data indicates [...] AI startup activity in the EU is geographically dispersed: 75 percent of analysed EU AI startups are spread across 95 cities, with no single hub dominating any market. Paris, Berlin, Amsterdam, Munich, and Stockholm are the largest hubs but together account for only a quarter of EU AI startups. EU city rankings across markets largely reflect general AI startup hub sizes rather than genuine market specialisation – but specialised niches do begin to emerge at the local level, e.g. Munich in manufacturing and supply chain and Barcelona in healthcare and life sciences. As our data skews towards larger startup hubs, specialisation patterns in smaller cities and regions may not be fully captured. Our findings have important implications for policymakers. Europe's challenges in AI competitiveness extend well beyond frontier models. The manufacturing and supply chain market, a strategic priority for the European Commission, presents grounds for cautious optimism, where existing industrial strengths align with national and regional specialisation signals based on AI startup activity, especially in Germany and Munich. The geographically distributed startup activity in the EU can support AI adoption across diverse regional economies and existing industrial strengths. At the same time, it raises questions about whether European hubs can achieve the scale and specialisation needed to compete globally. The technological diversity of AI also presents competitive opportunities that go beyond market specialisation alone. Whether the emerging specialisation patterns identified in this analysis develop into globally competitive positions will depend on continued access to growth capital, compute resources, and cross-border market integration." (Executive summary, pages 3-4) more

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From Crisis to Renewal: Addressing AI’s Impact on Our Information Ecosystem

Lena-Maria Böswald, Roa Powell, Tyreese Calnan

Berlin: Interface – Tech Analysis and Policy Ideas for Europe (2026), 18 pp.

"As AI summaries summarise information directly within search results, they can significantly reduce traffic to news publishers. Evidence suggests users are less likely to click through to original sources when AIOs are present on Google, threatening the financial viability of journalism. These impa ... cts from AI summaries and AIOs in particular are unlikely to be evenly distributed and will play out in different ways across the news sector across Europe, with smaller and ad-funded outlets particularly vulnerable, raising concerns about declining media plurality. At the same time, AI-generated summaries often fail to provide accurate and diverse information. Research shows that AI tools disproportionately rely on a narrow set of sources, marginalising smaller publishers. In addition, persistent issues with accuracy – including misleading or incorrect outputs – risk distorting public understanding and weakening trust in information systems. Current regulatory frameworks in both the EU and UK are struggling to address these challenges effectively. While multiple legal instruments are relevant and already in place – including competition law, platform regulation, copyright, and AI governance – none are fully equipped to respond quickly and comprehensively to the harms of monopolisation and the lack of remuneration for media outlets posed by AI summaries. We argue that competition policy offers the most effective and immediate route to intervention, with digital regulation serving as a secondary lever. The UK’s approach, based on flexible, case-specific competition rules, enables faster action through tailored conduct requirements for dominant firms like Google. In contrast, the EU’s framework is broader but slower, relying more heavily on traditional, time-intensive antitrust investigations and ongoing debates around copyright and remuneration for publishers. Both systems offer complementary strengths, and policymakers should learn from each other’s approaches. We therefore recommend a “competition-first” policy package to ensure fairer outcomes. Key measures include enforcing fair ranking and attribution practices, preventing unauthorised use of publisher content, and potentially requiring platforms to compensate news organisations. However, competition policy alone is not the key. We therefore propose additional long-term interventions, including greater transparency through “nutrition labels” for AI-generated content and increased public or democratic funding for journalism. These measures aim to strengthen the resilience and independence of the news sector, ensuring that trustworthy information remains accessible in an information ecosystem shaped by AI. 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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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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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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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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Step by Step: How SAFE AI contributes to trustworthy AI

CDAC Network; Alan Turing Institute; Humanitarian AI Advisory (2026), 8 pp.

"Based on the National Institute of Standards and Technology (NIST) AI Risk Management Framework, the seven characteristics of trustworthy AI are: 1. Valid and Reliable: The AI system should perform as intended, delivering accurate results consistently across different situations. 2. Safe: The sy ... more

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Environmental Cost of AI's Energy Use: Carbon, Water and Land Footprints

Miriam Aczel, Sanaz Chamanara, Mir Matin, Aria Farsi, Tshilidzi Marwala, Kaveh Madani

Richmond Hill, Ontario (CA): United Nations University Institute for Water, Environment and Health (UNU-INWEH) (2026), 55 pp.

"[...] The study reveals some striking numbers. In 2025, data centers—the physical backbone of AI—consumed an estimated 448 TWh of electricity. If data centers were a country, that level of electricity use would rank it 11th globally. On current trajectories, data center electricity demand could ... roughly double to 945 TWh by 2030, nearly triple the combined annual electricity use of Pakistan, Bangladesh, and Nigeria, together home to more than 650 million people. Producing that much electricity would have a carbon footprint of 399 million tonnes CO2e, requiring 6.7 billion trees grown over 10 years to offset—roughly twice the number of trees in the United Kingdom. The associated water footprint of 9.3 trillion liters would be equivalent to the annual domestic water needs of all 1.3 billion residents of Sub-Saharan Africa. The land footprint associated with this electricity would exceed 14,500 km², nearly 10 times the size of Mexico City. AI is now one of the most significant drivers of that data center growth. In 2025, AI workloads alone accounted for around 20% of total data center electricity use, and if that share rises to 40% by 2030 as projected, its electricity demand could reach roughly 378 TWh—enough to meet the residential electricity needs of the entire population of Sub-Saharan Africa for over 2 years. AI’s environmental impacts are shaped not only by data center growth and electricity supply mixes, but also by the escalating cost of building ever-larger models. For example, GPT-3 training consumed an estimated 1.3 GWh of electricity over 34 days, while GPT-4 is estimated to have consumed 50 to 70 GWh over 100 days, roughly 40 to 55 times GPT-3. Yet training is only part of the picture as AI’s operational footprint is increasingly driven by inference. Once models are deployed, billions of everyday interactions account for the bulk of energy use, with inference estimated at roughly 80–90% of total energy consumption. [...] The report identifies priority implications for key stakeholders. Governments must integrate AI infrastructure into energy system planning, carbon accounting, water governance and land use permitting. This requires standardized environmental footprint reporting so impacts can be verified and compared across providers and jurisdictions. Industry and AI developers should treat model selection, default outputs, and routing decisions as footprint determinants, while also improving efficiency-by-design. Users and deploying organizations also shape impacts through volume, frequency, and modality choices. They should adopt “fit-for-purpose” use: selecting the lightest model and lowestenergy format that meets the task and limiting high-cost features when not needed. Data center operators and utilities should recognize siting and procurement as environmental footprint decisions, apply environmental impact screening and cumulative impact assessment. They should implement transparent mitigation and community safeguards where expansion occurs. Investors and financiers should treat electricity, carbon, water, and land footprints as material risks for AI infrastructure portfolios and use comparable footprint metrics in due diligence." (Executive summary, pages 12-13) 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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Handbook on Anthropology and Artificial Intelligence
Deep Insights

Sahana Udupa, Peter Hervik (eds.)

Cheltenham (UK); Northampton, MA (US): Edward Elgar Publishing (2026), xix, 495 pp.

"This timely Handbook provides a groundbreaking collection of anthropological research on artificial intelligence (AI), examining how anthropology can help to comprehend and critique its development. Evaluating the limits, hopes and fears of AI, leading experts explore its influence as a sociocultur ... more

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Building an Open and Responsible Voice Technology Ecosystem: Policy Recommendations for Digital Inclusion in India

Bhashini; Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ); FAIR Forward – Artificial Intelligence for All (2026), 57 pp.

"This report examines key barriers to building open and responsible speech systems in India—from data collection and model development to infrastructure and responsible practices. It proposes policy recommendations and governance mechanisms to support an innovative and equitable voice-technology e ... cosystem. Key Recommendations: A. TREATING FOUNDATIONAL DATASETS AS PUBLIC GOODS: Foundational datasets for speech technologies are large, reusable corpora of audio, text, and metadata. They are curated to support a wide array of downstream applications, including automatic speech recognition (ASR), text-to-speech (TTS), and speech translation. Making foundational datasets available as digital public goods addresses market failures in voice-technology ecosystems and promotes local economic innovation. The report identifies challenges arising from the linguistic diversity and nuance of Indian languages, infrastructural barriers, the absence of common data standards that create governance gaps, and unresolved intellectual property and privacy issues. Policy recommendations for building foundational language datasets include clarifying and revisiting existing laws to enable the use of publicly available material, ensuring sustainable investments supported by government and blended finance techniques, and instituting strong governance systems with shared standards, coordinated repositories, and independent quality assurance. B. BUILDING OPEN AND REPRESENTATIVE MODELS: India’s success in inclusive voice technologies depends on whether speech systems perform well across the country’s linguistic and social diversity. Today, limited openly available datasets, inadequate evaluation benchmarks, and uneven access to compute have resulted in models that perform inconsistently across languages, accents, and demographic groups, especially those from rural, low-resource, or marginalised communities. Strengthening India’s ecosystem requires further investment in data, evaluation, and infrastructure. Benchmarking should be built around open evaluation datasets and transparent leaderboards providing common baselines for developers, improving procurement standards, and helping assess performance across diverse languages and speaker groups. On the infrastructure side, platforms such as Bhashini, ULCA, and AI Kosh offer strong foundations although effective operation hinges on sustained governance, clear access protocols, and long-term funding models. C. INSTITUTIONALISING SUSTAINABLE OPEN-SOURCE INFRASTRUCTURE: Speech datasets place far greater demands on storage, bandwidth, and compute than text data, making the financing and governance of longterm hosting a central challenge for India’s voice-tech ecosystem. A fragmented licensing landscape adds further uncertainty: overlapping or incompatible terms across data, code, model weights, and evaluation sets impose substantial compliance burdens on small actors, while enforcement gaps allow misuse with little recourse. Sustaining open, equitable development requires treating dataset hosting as durable public digital infrastructure rather than grant-based, project-specific assets. By international standards, India’s emerging platforms, such as AI Kosh, provide a remarkable foundation. However, they require longterm funding, transparent governance, and clear access pathways for non-government contributors. Collaborative stewardship models, such as the Mozilla Data Collective, can help establish shared quality norms and consistent metadata conventions. D. STRENGTHENING RESPONSIBLE DEPLOYMENT: Deploying speech technologies responsibly requires more than high-performance models; it depends on safe systems, contextually appropriate use, and clear accountability. Existing data practices lack value-sharing mechanisms, leaving communities and researchers without recognition or benefitsharing, even as their contributions fuel commercial products. Risks of misuse, including voice cloning, phishing, and deepfake-driven misinformation, are rising, and unintended harms like linguistic exclusion, biased performance across accents and genders, and the erosion of regional language identities remain widespread. Addressing these gaps requires structural guardrails that embed fairness, transparency, and accountability into deployment workflows. Preventing misuse demands a combination of technical safeguards, stronger legal pathways, and widespread public literacy efforts to help users recognise risks and exercise their rights. The report argues that a strong ecosystem requires more than innovation funding. Building open-source foundations—including language datasets, standards, collection protocols and responsible AI frameworks—promotes demand-driven local innovation. It is therefore essential that the state plays an active, shaping role, much as it has in the development of digital public infrastructure. In the context of voice technology, this involves both investing in commercially viable languages and sustaining low-resource languages that are vital for inclusion but unlikely to attract private capital. Open-source assets can reduce costs for the public and private sector alike. However, they demand long-term planning and financing for hosting, maintenance, and updates. These assets can be supported through blended-finance models that pool public, philanthropic, and commercial resources. Emerging national initiatives, such as the proposed AI marketplaces, can further structure participation, transparency, and value-sharing across data, annotation, and deployable models." (Executive summary, pages 9-11) more

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Indic Voice Technologies for an Inclusive Digital India: Toolkit for Developers

Nihar Desai, Sujith P., Harleen Kaur, Dona Mathew, Jyotsna Jayaram, Padmavathi Prasad, Thomas Vallianeth

Bhashini; Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ); FAIR Forward – Artificial Intelligence for All (2026), 70 pp.

"This toolkit sets out a layered, lifecycle-oriented approach to building inclusive and robust speech artificial intelligence (AI) systems. It brings together strategies for diverse and representative data collection, linguistically informed model training, rigorous quality control, and deployment o ... more

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Principled caution: How AI is used in public-interest media in Asia

James Gomez

Bonn: Deutsche Welle DW Akademie (2026), 52 pp.

"In conclusion, this research has provided a systematic understanding of AI adoption within public-interest media across Afghanistan, Bangladesh, Bhutan, Cambodia, Indonesia, Myanmar, Pakistan and Uzbekistan. By identifying concrete areas for strategic intervention, this research seeks to strengthen ... journalistic quality and prepare the sector for an AI-integrated future. The findings reveal that the AI adoption is currently taking place within public-interest media, but it is one of “principled caution” where adoption is careful, selective and highly varied. This evolution unfolds within a structurally precarious media environment where AI regulation is often absent or actively restrictive. While adoption remains cautious to protect journalistic credibility from risks such as hallucinations and biases, newsrooms are increasingly embracing AI as a powerful toolset to alleviate severe capacity and resource constraints. Currently, organizations primarily utilize AI for assistive, routine tasks such as translation, transcription, summarization, and early-stage content ideation and the final language editing stage rather than for broader organizational transformation. The research highlights deep capacity deficits in the governance framework, institutional training, and leadership. These gaps often prevent individual AI literacy from translating into consistent, organization-wide policy formulation and enforcement. This challenge is further exacerbated by the funding instability and resource constraints that characterize the region’s independent media, restricting the long-term investment necessary for ethical and responsible AI adoption. Despite these legal, political, technical, capacity, and operational constraints, localized and innovative AI workflows are emerging. While AI has not yet generated entirely new, revenue-generating business models, strategic investments in customized AI infrastructures, such as in-house dashboards and media monitoring systems, present a critical pathway toward long-term operational resilience. To support this trajectory, the report recommends coordinated action among media organizations, governments, technology companies, donors and media development funders to invest in long-term AI capacity, governance frameworks, and secure technological infrastructure." (Conclusion, page 39) more

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Fake friend: How ChatGPT betrays vulnerable teens by encouraging dangerous behaviour

Center for Countering Digital Hate (2025), 54 pp.

"ChatGPT is easily accessed by children without age restrictions or parental controls: ChatGPT says users must be at least 13 to sign up and have parental consent if aged under 18. However, ChatGPT does not verify users’ ages or record parental consent. ChatGPT generates harmful content within mi ... 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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The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of KnowledgeWorkers

Hao-Ping (Hank) Lee, Advait Sarkar, Lev Tankelevitch, Ian Drosos, Sean Rintel, Richard Banks, Nicolas Wilson

Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI25) (2025), 23 pp.

"The rise of Generative AI (GenAI) in knowledge workflows raises questions about its impact on critical thinking skills and practices. We survey 319 knowledge workers to investigate 1) when and how they perceive the enaction of critical thinking when using GenAI, and 2) when and why GenAI affects th ... more

„Hi, how may I assist you?” Chatbots for Better Service Delivery. Practical Guide, Applications and Principles in Development Cooperation Key Guides

Handbook on Anthropology and Artificial Intelligence Deep Insights

„Hi, how may I assist you?” Chatbots for Better Service Delivery. Practical Guide, Applications and Principles in Development Cooperation
Key Guides

Handbook on Anthropology and Artificial Intelligence
Deep Insights