This view offers a detailed, individual perspective on each of the SDG indicators, allowing you to break down and better understand the parameters within both global and local contexts. It enables you to track the evolution of each indicator over time, providing valuable insights into how it has progressed across different regions and countries.

By selecting the SDG-related indicators from the dropdown menus, you can delve deeper into the data. Scroll down to view the curves of the selected indicators represented for each country, allowing for a comparative analysis of how various nations are performing on specific SDG targets. The data is sourced from well-established open data providers such as UN Stats and the World Bank, through the SDG Tracker initiative of Our World in Data. This ensures that the insights are based on reliable and comprehensive sources, although it’s important to note that the data coverage may not be complete for all countries. Despite this, the visualization provides a valuable signal that can be used to explore multilevel relationships between different SDG indicators.

This work was prepared in the context of the project An AI-driven Observatory Against Poverty granted by the

This view presents the influence of multiple factors on the selected indicator, measured using SHAP (SHapley Additive exPlanations) values—a powerful method that offers transparent, data-driven insights into the predictions of complex models. The visualization showcases the top 25 features that most significantly contribute to changes in the selected indicator, providing clarity on which variables have the greatest impact.

By selecting a specific country and year, you can analyze how multiple indicators collectively affect the poverty baseline, offering a comprehensive understanding of the underlying drivers of poverty. This feature allows you to explore the interactions between different factors and how they influence poverty outcomes. Additionally, the tool allows you to toggle between two different plot types, providing complementary perspectives on the data. This flexibility enables more nuanced exploration, helping policymakers and analysts to gain a deeper understanding of the relationships between indicators and make more targeted, evidence-based policy decisions.

This work was prepared in the context of the project An AI-driven Observatory Against Poverty granted by the

This view offers an in-depth analysis of the causal relationships between critical socioeconomic and environmental indicators, using a transfer entropy map. Grounded in data from three influential factors, this map helps identify the directional influences of one indicator on another, providing valuable insights into the underlying dynamics of complex systems.

By selecting any two indicators, you can explore how they influence each other across different regions, revealing geographic patterns, interdependencies, and potential areas of concern or opportunity. This capability allows you to uncover the systemic interactions that drive changes in key metrics, offering a deeper understanding of the factors at play in sustainable development. This tool’s insights are invaluable for evidence-based policy decisions, supporting targeted interventions, and more efficient resource allocation. Policymakers can use the visualized data to prioritize actions and allocate resources where they will have the most significant impact on socioeconomic and environmental outcomes.

This work was prepared in the context of the project An AI-driven Observatory Against Poverty granted by the

This view provides a dynamic perspective on the progress of this SDG, leveraging advanced Markov Chain analysis to model the relationships between countries and their states within the broader SDG context. Using the Streamstory.ijs.si technology, this tool visualizes the transitions between different states, revealing how countries move through various stages and hierarchies over time.

The data-driven visualization highlights key patterns and interdependencies between countries, offering valuable insights into how progress—or lack thereof—affects global and regional trends. By configuring the time window, users can explore changes over different periods, uncovering seasonal trends and long-term shifts in the data. This tool enables policymakers to understand the dynamics at play between countries and their progress on this SDG, allowing for more informed, targeted decision-making that accounts for historical patterns and future projections.

This work was prepared in the context of the project An AI-driven Observatory Against Poverty granted by the

This view allows you to delve into the various topics and subtopics highlighted in global news coverage related to the chosen Sustainable Development Goal (SDG). By analyzing signals from worldwide media, this tool offers a comprehensive understanding of how different issues related to the SDG are being discussed across the globe.

The data is provided in collaboration with Event Registry, a cutting-edge AI news engine that aggregates and analyzes news content. Through this partnership, we bring you real-time insights into the evolving narrative surrounding SDG progress, allowing policymakers to stay informed about key global developments. For further exploration, you can visit eventregistry.org, where you can access more visualizations related to worldwide news queries and even experiment with their News API to gain deeper, customizable insights into global media trends.

This visualization offers a comprehensive view of the intensity of global news coverage on the key topics related to the selected SDG, highlighting how AI influences these areas. It tracks the newsworthiness of AI’s impact on the SDG across regions and over time, providing an insightful look at how AI-driven solutions and innovations are shaping global discussions. By selecting a specific SDG topic and using the interactive slider, you can explore the geographic distribution of news coverage, allowing you to see where these issues are most prominently featured in the media.

This tool is developed in collaboration with Event Registry, an advanced AI news engine, as part of the European Commission-funded ELIAS project (European Lighthouse of AI for Sustainability). ELIAS aims to promote AI as a key enabler for sustainability, and this visualization is an essential resource for policymakers to stay informed about how AI is being covered and perceived globally in relation to SDGs.

This interactive data visualization allows you to explore worldwide news, social media posts, and forum discussions on SDG-related topics. By leveraging advanced search and filtering tools, you can refine your queries and gain deeper insights into the global conversations surrounding each SDG.

Use the search bar and search pointer to navigate through the available data, and click on individual items to access the original sources of information. This feature is designed to help you identify trends, understand public discourse, and pinpoint key issues that are emerging in relation to SDGs. This visualization is developed in collaboration with the Department of Artificial Intelligence at the Institute Jozef Stefan, as part of the European Commission-funded RAIDO project. RAIDO focuses on harnessing the power of AI to enhance data-driven decision-making for sustainable development, empowering policymakers with up-to-date, evidence-based insights.

In this view, you can explore the research trends related to the SDG you’ve selected, represented through a Gantt chart. This visualization highlights the 15 most relevant trends identified within the chosen SDG, some of which may also intersect with other SDGs.

These interconnected trends are color-coded for easy identification, providing a comprehensive view of how various global issues are evolving in relation to one another. The data used for this analysis is sourced from OpenAlex, a comprehensive research database containing over 128 million articles published worldwide since the 1940s. This extensive dataset offers valuable insights into the main research topics that shape the discourse on the SDG, allowing policymakers to track the progression of academic and scientific focus over time and understand how these trends evolve and intersect. By visualizing these trends, policymakers can stay informed about emerging areas of research and their relevance to sustainable development goals, enabling data-driven decision-making that considers both historical context and future directions.

This visualization allows you to observe the relationships between key concepts (represented as edges) relevant to the selected SDG. These connections reflect the strength of relationships between concepts, with the intensity of the link indicating how frequently these topics co-occur in scholarly articles. Stronger connections suggest that the concepts are frequently discussed together in the context of the SDG, while weaker links show less frequent co-occurrence.

The data for this analysis is sourced from OpenAlex, a comprehensive research database encompassing over 128 million articles published globally since the 1940s. By analyzing these relationships, policymakers can gain deeper insights into how different concepts within the SDG are interconnected, helping to identify areas of high research focus as well as potential gaps. Originally developed as part of the European Commission-funded NAIADES project, this tool was initially focused on SDG 6 (Clean Water and Sanitation). It has since been expanded to cover all 17 SDGs, providing a broad, cross-cutting view of research trends and interrelated concepts across the entire sustainable development agenda. This resource empowers policymakers to understand the nuanced relationships between concepts, guiding more informed decisions and fostering an integrated approach to SDG implementation.

This interactive data visualization allows you to explore published research and submitted patents related to SDG topics, helping to build global knowledge on sustainable development. By leveraging advanced search tools, you can refine your queries and explore a wide range of data, including the latest research articles and innovative patents that align with SDG goals. Use the search bar and search pointer to navigate through the available data, and click on individual items to access the original sources. This tool provides a comprehensive view of the academic and technological advancements contributing to SDG progress, offering policymakers valuable insights into emerging innovations and research trends.

Developed in collaboration with the Department of Artificial Intelligence at the Institute Jozef Stefan, this visualization is part of the RAIDO project, funded by the European Commission. RAIDO focuses on harnessing AI to drive data-driven decision-making for sustainability, enabling stakeholders to understand the cutting-edge research and innovations shaping the future of SDGs.

This view allows you to observe the current weight of various SDG topics in the policy and legislative landscape for the selected SDG. The interactive SDG barcode visually represents the prominence of different topics within policies related to the chosen SDG.

By hovering your mouse over the rectangles in the barcode, you can identify and explore the specific SDG topics being prioritized in current policies and legislation. The initial policy data is sourced from OECD.AI, offering a comprehensive overview of policy trends and legislative priorities. This analysis is part of the AI4GOV project, funded by the European Commission, which aims to integrate AI-driven insights into government decision-making processes, ensuring that policies align with the SDG framework. This tool provides policymakers with valuable insights into how different SDG topics are being addressed at the policy level, helping to guide more targeted and effective legislative action for sustainable development.

This view allows you to observe the evolving relationships between key concepts identified in legal and regulatory documents related to the selected SDG. By analyzing the frequency of these concepts within policy documents, you can gain insights into how different topics are interrelated and how their prominence shifts over time in the context of the chosen SDG.

The visualization helps you explore how concepts are linked within the policy landscape, providing an understanding of the areas that are receiving more focus and those that might need further attention. The relationships between these concepts are mapped according to their frequency and the SDG they correspond to in the policy documents. The initial policy data is sourced from OECD.AI, offering valuable insights into policy trends and legislative frameworks. This analysis is part of the AI4GOV project, funded by the European Commission, which aims to integrate artificial intelligence into government processes for more informed, data-driven decision-making on SDG implementation. This tool provides policymakers with a dynamic view of how SDG-related concepts are being addressed and prioritized within the legal and regulatory landscape, enabling more effective policy adjustments and interventions.

This interactive data visualization allows you to explore global research and patent submissions related to SDG topics, helping to build a comprehensive knowledge base for sustainable development. Through advanced search tools, you can refine your queries to uncover the most relevant research articles and technological innovations that align with the SDGs. By using the search bar and pointer, you can easily navigate through the data and explore specific trends, breakthroughs, and solutions. Clicking on individual items will take you directly to the original sources, enabling deeper exploration of the research and patents that are driving progress in SDG-related fields.

This tool is developed in collaboration with the Department of Artificial Intelligence at the Institute Jozef Stefan, as part of the RAIDO project, funded by the European Commission. RAIDO seeks to harness AI to enhance data-driven decision-making for sustainability, providing policymakers with the insights they need to inform and guide effective strategies for achieving SDGs.

Avoiding data bias in AI systems is crucial for ensuring fair, accurate, and equitable outcomes. Without addressing bias, AI systems can unintentionally reinforce existing inequalities, leading to unfair or skewed results. This can hinder efforts to achieve the SDG 1: No Poverty by disproportionately affecting vulnerable communities. In this dashboard, we analyze the bias present in the data ingested by this observatory for SDG 1. By providing insights into potential biases, the tool helps ensure that AI systems are trustworthy, inclusive, and capable of driving equitable solutions. This enables policymakers, researchers, and organizations to make informed decisions based on fair and unbiased data, ultimately supporting more inclusive and sustainable development efforts.

For analysis we use OECD AI Policy documents. Some of those documents are very large, and we split each document into smaller parts (so called “chunks”), which can contain multiple paragraphs. The reason for this is to prepare data for easier analysis with large language models, so called Retrieval-Augmented Generation (RAG). RAG is an advanced technique that combines retrieval-based methods with generative models to improve the performance of tasks such as question answering, text generation, and other natural language processing (NLP) applications. For each chunk then the sentiment is computed based on VADER (Valence Aware Dictionary and sEntiment Reasoner) methodology. Since VADER is known to have weak multilingual capabilities, all the documents were machine translated into English first.

While the results of this procedure are reliant not only upon the accuracy of the sentiment analysis tool, but also upon the accuracy of machine translation, it is important to stress that sentiment analysis is less sensitive to common machine translation problems than other usages, because sentiment analysis usually focuses on identifying the polarity (positive, negative, neutral) of a text rather than understanding its full semantic content. Also, sentiments in text are often expressed redundantly, which can help mitigate the impact of translation errors. As a result, minor translation errors that do not alter the overall sentiment and do not significantly impact the sentiment analysis is possible.

For the purpose of this analysis, they computed the average sentiment of (chunks of) AI policy documents for each country. We are presenting the visualisation of average sentiment of countries’ AI policy documents on the map. Since AI policy documents are mostly documents of legal nature (acts, policies, regulatory and governance frameworks), the sentiment should be mostly neutral, however, the analysis shows that there are country differences.

VADER computes positive, negative and neutral sentiment. Each of those values are between 0 and 1. The score indicates the proportion of text that is considered positive, negative and neutral. The sum of negative, positive, and neutral sentiment scores always equals 1, however in practice the sum of three sentiment scores can sometimes slightly exceed or fall below 1 due to floating-point precision errors or rounding issues that occur during computation.

 

Developed in collaboration with the European Commission project 

This interactive data visualization allows you to explore open educational resources that focus on SDG-related topics, providing insights into which SDGs are most strongly represented in educational content. The tool helps you navigate through a wealth of knowledge, enabling you to identify courses, lectures, and materials that align with your SDG priorities.

The data is sourced from Videolectures.net, an award-winning UNESCO platform that hosts over XX recorded and preprocessed lectures on a wide array of SDG-related subjects. This platform offers a comprehensive selection of educational content, allowing users to explore a range of topics that are crucial for advancing sustainable development. By using the visualization, you can discover which SDGs are most frequently covered in the available resources, facilitating access to high-quality educational content that can support SDG learning and capacity-building efforts. This tool is an invaluable resource for policymakers, educators, and researchers aiming to expand their knowledge of SDG-related issues and integrate this learning into their work.

This view allows you to explore the diverse SDG topics identified in available open educational resources and their distribution across the SDGs, specifically related to the SDG you have selected. It provides an interactive way to analyze how different SDG topics are represented in educational content, helping users understand the scope and reach of SDG-focused learning materials.

The data is sourced from Videolectures.net, a UNESCO award-winning platform that offers over XX recorded and preprocessed lectures covering a wide variety of SDG-related topics. These resources are designed to support in-depth learning and understanding of sustainable development issues, offering valuable educational content for policymakers, educators, and researchers. By engaging with this tool, you can gain a clearer perspective on the SDG topics most covered in the available resources, facilitating the discovery of relevant courses and lectures to deepen your knowledge of SDG challenges and solutions.

This interactive data visualization enables you to review the latest published research and submitted patents on SDG-related topics, fostering the development of global knowledge. By using advanced search filters, you can refine your queries and uncover the most relevant academic articles and technological innovations driving progress in the pursuit of SDGs. Leverage the search bar and search pointer to explore the available data more efficiently, helping you identify key trends, breakthroughs, and emerging solutions. Clicking on individual items allows you to access the original data sources, ensuring transparency and providing a direct link to the research and patents that are shaping sustainable development efforts.

This tool is developed in collaboration with the Department of Artificial Intelligence at the Institute Jozef Stefan, as part of the RAIDO project, funded by the European Commission. The project aims to integrate AI-driven insights into research and innovation for sustainability, equipping policymakers, researchers, and institutions with the tools they need to make informed, data-driven decisions that advance SDGs.

This visualization provides an in-depth look at the various topics engaged by different actors within the existing innovation ecosystem focused on AI and sustainability. It highlights the key areas of innovation, research, and development that are driving progress in the intersection of AI and sustainable development.

The data is powered by IRCAI’s Top 100, which compiles the most influential organizations and initiatives contributing to AI and sustainability. Using sophisticated data analytics and machine learning methods, this tool helps users identify emerging trends, key players, and critical areas of focus within the innovation landscape. By exploring this visualization, you can better understand how different actors—such as research institutions, enterprises, and governmental bodies—are tackling sustainability challenges with AI technologies, providing valuable insights into the current state of innovation in the field.

In this view, you can explore the various topics and subtopics associated with existing initiatives that are dedicated to advancing specific objectives related to the selected SDG. This tool provides a detailed look at the ongoing efforts and innovations that are making significant contributions to the achievement of the SDG.

The data is sourced from IRCAI’s Top100 initiative, which highlights the most relevant AI and SDG-related initiatives from 2022 to 2024. This collection showcases key projects, innovations, and collaborations that are addressing sustainability challenges using AI and other cutting-edge technologies. By engaging with this tool, you can gain a deeper understanding of how different initiatives are aligned with the progress of the SDG, as well as identify the key areas where AI-driven solutions are being applied to achieve specific SDG targets.

This interactive data visualization enables you to explore the content of various initiatives within the ecosystem that are advancing SDG-related topics. By leveraging sophisticated data analytics, you can gain valuable insights into how different actors are contributing to sustainable development and how their efforts are aligned with the SDGs. The tool helps you make sense of the topics related to each initiative, offering a clearer view of where synergies exist and where further cooperation can be fostered. Use the search bar and search pointer to navigate through the available data, and click on individual items to access the original data sources, ensuring full transparency and easy access to relevant initiatives.

This tool is developed in collaboration with the Department of Artificial Intelligence at the Institute Jozef Stefan, as part of the RAIDO project, funded by the European Commission. RAIDO leverages AI to enhance data-driven decision-making for sustainability, offering policymakers, researchers, and organizations the tools to foster meaningful collaboration in addressing SDG challenges.