By analyzing production and consumption data, AI can enhance supply chain efficiency and minimize wastage. In this chart we compare the industrial progress worldwide (including construction), observing the value added per worker (constant 2010 US$).

This perspective offers a real-time view of the worldwide news on industrialisation-related topics, providing us with a media perspective on the progress at the different fronts of SDG 9.

 

Developed in collaboration with 

A SDG Live Report is what it is all about! Here, you can have a real-time perspective of the state of the world in regard to AI and sustainability, tracking some of the main issues to address in the near future.

 

AI has had a profound impact on the progress of Sustainable Development Goal 9 (SDG 9), which focuses on building resilient infrastructure, promoting inclusive and sustainable industrialization, and fostering innovation. Between 2015 and 2023, there have been 492,543 scientific publications exploring the role of AI in these areas. These studies underscore AI’s potential to revolutionize industrial processes, enhance infrastructure management, and drive technological innovation. AI technologies have been pivotal in optimizing supply chains, improving manufacturing efficiency, and enabling predictive maintenance of infrastructure. With 314,686 news articles covering the impact of AI on SDG 9, there is substantial public awareness and interest in how AI is transforming industry and infrastructure. Furthermore, the development of 1,656 AI policies targeting SDG 9 highlights a significant commitment from policymakers to integrate AI into industrial and infrastructural strategies, ensuring sustainable development and innovation.

Looking ahead, the next 5 to 10 years are expected to see further advancements in AI applications for industry and infrastructure. AI-driven innovations will continue to enhance manufacturing processes through automation and real-time data analytics, leading to increased efficiency and reduced environmental impact. The deployment of AI in smart infrastructure will improve urban planning and resource management, enabling cities to become more sustainable and resilient. Additionally, AI will play a crucial role in advancing research and development, fostering a culture of innovation that drives economic growth. Policymakers will need to focus on creating an enabling environment for AI adoption, addressing challenges such as data privacy and cybersecurity. Collaboration between governments, industry stakeholders, and research institutions will be essential to ensure that AI-driven advancements contribute to inclusive and sustainable industrialization, aligning with the goals of SDG 9. As AI technologies continue to evolve, their integration into infrastructure and industry will be key to achieving a sustainable and innovative future.

 

Developed in collaboration with the European Commission project 

Here is your window to the impact of AI in Industry. This dashboard is configurable and can be integrated into other systems to bring actionable technology into the systems of research institutions, governments and enterprises that are willing to make a difference.

 

 

Developed in collaboration with the European Commission project 

Avoiding data bias in AI systems is crucial to ensure fair, accurate, and equitable outcomes, preventing the reinforcement of existing inequalities and enabling more trustworthy and inclusive technologies. Here you will see a dashboard analysing the bias related to the data ingested in this observatory for SDG 9.

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