"[... ] la presente guÃa se fundamenta en el marco [conceptual europeo] DigComp 2.2, que establece un estándar claro sobre las competencias digitales necesarias para interactuar en el mundo actual. A lo largo de este documento, abordamos cinco competencias clave: búsqueda y gestión de informaciÃ
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³n, comunicación y colaboración, creación de contenidos digitales, seguridad y resolución de problemas. Cada una de estas áreas no solo es fundamental para el desarrollo personal y profesional, sino que también es crucial para fomentar una ciudadanÃa digital responsable." (Tapa posterior)
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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
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pacts of artificial intelligence (AI) systems. While several advances in methodology and tooling are evident, the review highlights substantial inconsistencies in how different lifecycle stages of AI are measured, analysed, and reported.
[.] One of the most pressing issues uncovered is the widespread reliance on indirect estimates when assessing energy consumption during the training phase of AI models. These estimates often lack real-time, empirical measurement. Furthermore, equally important lifecycle stages — such as inference (the operational use of models), Scope 3 emissions (from supply chains and hardware manufacturing), and infrastructure-level impacts (such as water consumption and cooling) — remain significantly underexplored. This reliance on proxies introduces substantial data gaps, impedes accountability, and restricts consumers’ ability to make informed, sustainable choices about AI.
To address these issues, the report uses a lifecycle-based approach, dividing the AI system's environmental impact into three stages: 1. Training, 2. Inference, 3. Supply Chain. For each stage, we examine measurement methodologies, identify current limitations, and offer recommendations for key stakeholder groups: developers (producers), users (consumers), and policy-makers. The overarching aim is to ensure that sustainability becomes a foundational element — embedded from the earliest stages of AI design to its deployment and continued use — rather than an afterthought." (Executive summary, pages v-vi)
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"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
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I solutions that bring tangible benefits to people and communities. A key part of the initiative involves the sharing of use cases, impactful AI applications and global lessons and expertise from around the world. In 2025, building on the success of its first edition, the project expanded its scope through an open call for AI use cases and AI Scholars. We received 234 use case submissions from 32 countries, out of which 160 were selected for inclusion in this interim report. These use cases span eleven key domains and reflect both the diversity of global innovation, regional solutions with lessons learnt and the practical ways in which AI is being applied to solve real-world problems." (Foreword)
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"The development and deployment of large language models like ChatGPT across the world requires expanding data centers that consume vast amounts of electricity. Using descriptive statistics and a multi-country computable general equilibrium model (IMF-ENV), we examine how AI-driven data center growt
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h affects electricity consumption, electricity prices, and carbon emissions. Our analysis of national accounts reveals AI-producing sectors in the U.S. have grown nearly triple the rate of the private non-farm business sector, with firm-level evidence showing electricity costs for vertically integrated AI companies nearly doubled between 2019-2023. Simulating AI scenarios in the IMF-ENV model based on projected data center power consumption up to 2030, we find the AI boom will cause manageable but varying increases in energy prices and emissions depending on policies and infrastructure constraints. Under scenarios with constrained growth in renewable energy capacity and limited expansion of transmission infrastructure, U.S. electricity prices could increase by 8.6%, while U.S. and global carbon emissions would rise by 5.5% and 1.2% respectively under current policies. Our findings highlight the importance of aligning energy policies with AI development to support this technological revolution, while mitigating environmental impacts." (Abstract)
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"As global stakeholders from governments, international organizations, the private sector, academia, and civil society, we convene in Hamburg to shape a human-centric, human-rights-based, inclusive, open, sustainable, and responsible AI future. We commit to advancing AI for the SDGs, aligning with o
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ngoing international efforts. A responsible AI future must be built on equal and meaningful participation, with actions to ensure that all stakeholders, especially those from emerging markets, developing economies, and vulnerable groups, have fair and equitable access to, as well as ownership of, computing, data, investment, and resources for capacity and talent development. AI’s benefits must not remain concentrated among a privileged few. We are committed to bridging digital divides and empowering all nations and communities to co-create and leverage AI solutions and evaluations that serve people and the planet. To achieve this, we call for leveraging AI responsibly, inclusively, and sustainably, aligned with the five pillars of the 2030 Agenda for Sustainable Development: people, planet, prosperity, peace, and partnerships." (A Joint Vision)
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"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
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AI responsibly. That progress is visible not only in the soaring number of UN AI initiatives — now totalling 729 projects, from 406 last year — but also in the depth of engagement across the system, with 53 UN entities contributing to this year’s Activities Report, six of them for the first time. This gathering momentum reflects our shared conviction that AI is no longer a distant aspiration; it is a present-day imperative within and beyond the United Nations. The projects highlighted here tackle urgent challenges, from accelerating climate action to improving health outcomes to expanding educational access, bolstering governance, and creating decent work. Nearly half of these efforts are built on partnerships with governments, academia, industry, and civil-society organizations, underscoring the multistakeholder spirit at the heart of the UN’s inclusive digital transformation agenda. Across our work, AI is already driving efficiencies, revealing deeper insights, and informing faster responses — from chatbots that streamline public-service delivery to data tools that strengthen emergency responses to applications that keep meetings and consultations running smoothly. These innovations are extending the reach and impact of our work in practical, promising ways. Yet the UN remains keenly aware of the risks and responsibilities that accompany AI. Ethics, human rights, and inclusion anchor every initiative, guiding us toward a shared digital future where AI helps us advance opportunity and prosperity for all. This report — prepared by the International Telecommunication Union (ITU) in partnership with 52 other UN entities — embodies the system’s resolve to lead by example. Read on to discover how AI is already driving progress, delivering results, and reshaping the way the UN serves people and planet in the digital age." (Foreword)
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"Ist Künstliche Intelligenz nur eine weitere Entwicklung der Digitalisierung des Alltags, eine effiziente Software in einer „Cloud“? Die KI-Forscherin Kate Crawford entlarvt diese verharmlosende Vorstellung und beleuchtet die konkreten Auswirkungen der Technologie auf die physische Welt. Ihre R
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echerche bietet Einblicke in den Bergbau, wo für die KI reale Ressourcen wie Gestein, Lithium-Sole und Erdöl abgebaut werden, und in Logistik-Zentren, wo menschliche Arbeitskraft für Unternehmensgewinne ausgebeutet werde. Künstliche Intelligenz gehe, so Crawford, mit Umweltzerstörung und Raubbau an der Natur sowie sozialer Ungerechtigkeit einher, etwa der Ausbeutung von Clickworkern. Zudem basiere Künstliche Intelligenz auf einer Klassifizierung von Daten, die keineswegs einer objektiven Logik folge, sondern Hierarchien perpetuiere und Ungleichheiten verstärke. Angesichts dieser Verzerrungen könne KI nicht als neutrale Technologie angesehen werden, deren Probleme wiederum durch weitere technische Innovationen gelöst werden können. Vielmehr seien politische Regulierung, demokratische Kontrolle und die Einbeziehung der von Vorurteilen, Diskriminierung und Ausbeutung Betroffenen erforderlich. Die bereits existierenden Gefahren der Technologien für Privatsphäre, Menschenrechte, Natur und Arbeitswelt seien weitaus bedrohlicher als die von manchen befürchtete Entwicklung einer künstlichen Superintelligenz." (Back cover)
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"The Oxford Handbook of Digital Ethics is a lively and authoritative guide to ethical issues related to digital technologies, with a special emphasis on AI. Philosophers with a wide range of expertise cover thirty-seven topics: from the right to have access to internet, to trolling and online shamin
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g, speech on social media, fake news, sex robots and dating online, persuasive technology, value alignment, algorithmic bias, predictive policing, price discrimination online, medical AI, privacy and surveillance, automating democracy, the future of work, and AI and existential risk, among others. Each chapter gives a rigorous map of the ethical terrain, engaging critically with the most notable work in the area, and pointing directions for future research." (Publisher description)
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"The new series Digital Progress and Trends Report adopts a holistic framework of digitalization, with selective topics examined in depth in each edition. The framework [...] includes both the production and the adoption sides of digital technologies and their interactions. Box ES.1 explains how the
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series supports global efforts to study the progress, gaps, benefits, and risks of digitalization. The benefits and risks are also interconnected and reflect the trade-offs and complexity of digitalization: innovation and growth can be accompanied by high concentration and reduced market contestability. Efficiency gains and lower costs for large businesses may mean higher inequality and polarization. Digitalization can create jobs and improve inclusion, but it also results in power asymmetry and makes it easier for governments and companies to monitor and control individuals. Digital innovation creates new possibilities for climate change mitigation and adaptation but expands the carbon footprint of the information and communication technology (ICT) sector. Cybersecurity, privacy, and misinformation are also major risks that can undermine trust in the digital space and circumvent the gains from digitalization. Countries need to maximize the benefits while minimizing the risks of digitalization." (Executive summary)
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"The progress of artificial intelligence, particularly with generative AI models, has provoked intense reactions, regardless of whether they are based on the logic and functioning of the technology. Unlike predictive AI, generative AI produces original content by synthesizing texts, images, voices,
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videos, and code from large databases and may significantly impact the creative economy. This study introduces the basic concepts of AI and generative AI (including a taxonomy of generative models) and outlines the distinction between image or video and text production techniques. The central argument of this study claims that the cultural fuss is not accidental, defending the hypothesis that the advent of generative AI places humanity amidst the crossing of its fourth narcissistic wound."(Abstract)
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